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Machine Learning Engineer - Inference Maintainer & Developer Experience

Roboflow

Location: USA Advertised pay: USD 155,000–180,000 / yearly Work arrangement: Region-restricted remote Employment: Full Time Posted: November 2, 2026 Freshness: Very fresh Source check: Observed in the past 3 days
Disclosure4 of 5 core facts statedReview uncertainties
Last source observationObserved in the past 3 daysObservation is not employer confirmation.
Application destinationjobicy.comReview the application route and source evidence in the full report below.

Before relying on the listing: Confirm Application closing time with the employer or source. See verification questions

Executive opportunity brief

The decision in one screen

A compact evidence-first read of compensation, flexibility, mobility and what still needs verification. No universal quality score is used.

Compensation positionAround the current median105 current same-currency observations
Work flexibilityRegion-restricted remoteBased only on stated location/work-arrangement evidence.
Decision evidence4 of 5 core facts knownThis listing states more core decision facts than the median role in the current sample.
Mobility evidenceSponsorship not establishedUnknown evidence is not treated as unavailable.
Current alternatives1222 current same-family rolesCurrent HPJ inventory, not the whole labor market.
Negotiation readinessNegotiation evidence available: advertised pay range, current same-currency market context, strong decision evidence.Use the private Offer & Negotiation Lab below if an offer arrives.

Fast read

Opportunity DNA

The available details do not point to a single standout advantage yet. The clearest issue to weigh is location rules could change the fit.

PayRange available

Current comparable position is around the 50th percentile.

FlexibilityLocation limits visible

Geographic eligibility is restricted to a country or region.

Entry pathRequirements unclear

The listing does not make the minimum education or experience threshold clear enough yet.

MobilityNot established

Sponsorship and relocation support are not clear from the current listing.

ApplicationSome process detail

Part of the application route can be assessed, but the full external process may contain additional steps.

TraceabilityCurrent source traceable

At least one recent enabled source can still renew this vacancy.

This fingerprint summarizes what is clear, constrained or still unanswered in the current listing. It is a reading aid, not an overall job score.

Decision evidence check

4 of 5 core facts stated

This checks whether the listing states the facts that commonly change an employment decision. It measures disclosure completeness, not whether the opportunity is good or bad.

Facts already stated

  • Compensation
  • Work arrangement
  • Advertised location
  • Employment type

What to verify next

  • Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?

Advertised location is not proof of work authorization, sponsorship, or legal eligibility. Unknown information is not a negative score; verify those questions separately.

Decision levers

What could make or break the fit?

Reasons to look closer

Nothing stands out strongly enough yet to call a clear advantage. The role may still be attractive for reasons the listing does not spell out.

Trade-offs to weigh

  • Location rules could change the fit: Geographic eligibility is restricted to a country or region.

Questions worth asking before you invest more time

  • Confirm whether a degree is actually required, preferred, or replaceable with equivalent experience.
  • Confirm the actual minimum experience requirement rather than assuming preferred background is mandatory.
  • Ask whether visa sponsorship is available before investing significant time in the application.

These questions come from details the listing leaves open. An unanswered point is not treated as a disadvantage.

Opportunity neighborhood

Keep your options open

These live roles sit close enough to this opportunity to make useful comparison points. Look across several employers before deciding what pay, flexibility or access trade-offs you are willing to accept.

Data Engineer & PowerBI Developer

LUZA Group · Remote — Worldwide

Salary not disclosed Worldwide remote 4/5 core facts

Why it is nearby: Similar work arrangement · Same employment type · Recently posted

Machine Learning Data Scientist posting

Differential.bio · Munich

Salary not disclosed On-site / hybrid 3/5 core facts

Why it is nearby: Same employment type · Recently posted

Different angle: Seniority is not explicit · Different work arrangement

Applied AI Engineer

Resonance · Berlin

Salary not disclosed On-site / hybrid 3/5 core facts

Why it is nearby: Same employment type · Recently posted

Different angle: Seniority is not explicit · Different work arrangement

Senior BI Analytics Engineer

Carwow · London

Salary not disclosed On-site / hybrid 3/5 core facts

Why it is nearby: Same employment type · Recently posted

Different angle: Different work arrangement

This neighborhood is drawn from current inventory in the same broad occupation. Similarity is based on explicit listing facts; missing details do not count against a job.

Role market context

How much current choice is visible around this kind of work?

This compares the vacancy with current High Paid Job inventory in the broad Data & AI roles family. It describes visible choice and disclosure, not future demand or career quality.

Current visible roles1,222

200 most recent current roles sampled for the disclosure measures below.

Employers represented141

Distinct employers in the bounded current sample, not an estimate of all employers hiring in the market.

Decision-ready disclosure25%

Current sampled roles stating at least four of the five Core Decision Facts.

Compensation stated19%

Requires a numeric amount, explicit ISO currency and supported pay period.

Work arrangement stated95%

Listings with explicit enough remote, hybrid or on-site evidence to classify.

Stated closing time19%

Missing deadline evidence remains unknown and is not interpreted as an open-ended application window.

Disclosure comparison: This listing states more core decision facts than the median role in the current sample. This listing: 4/5 · sample median: 3.0/5.

Currencies with explicit pay evidence in the sample: USD (22) · EUR (13) · GBP (2) · CAD (2). Raw amounts across different currencies are not compared.

This describes current visible High Paid Job inventory in the same broad occupation family. Percentages use a bounded recent sample and are not a forecast, a complete labor-market census or an employer-quality measure.

Constraint opportunity lab

What changes the size of the current opportunity pool?

Each line applies one stricter evidence-based constraint to the same bounded current role-family sample. It helps you see the trade-off in visible choice without deciding which trade-off is right for you.

Require explicit sponsorship or relocation support 2 / 200 1%

Counts only affirmative or possible sponsorship evidence, or relocation explicitly offered. Unknown is not treated as no.

Require worldwide remote evidence 19 / 200 10%

A much stricter subset: only listings with explicit worldwide-remote evidence remain.

Require usable compensation disclosure 38 / 200 19%

Requires amount, explicit ISO currency and a supported pay period. Missing pay evidence remains unknown.

Require at least four Core Decision Facts 50 / 200 25%

Shows the subset with enough stated evidence for a more informed first comparison.

Keep this advertised salary floor in the same currency 6 / 20 30%

Only same-currency opportunities with usable annualized pay enter this comparison. Other currencies are not converted or treated as lower.

Require a remote-listed arrangement 59 / 200 30%

Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.

Stay in the same stated country 21 / 65 32%

Uses only roles where a country is known. Roles without country evidence are excluded from this denominator.

Keep the same employment type 116 / 124 94%

Uses only roles with a stated employment type.

These are counterfactual filters over a bounded sample of current HPJ inventory in the same broad occupation family. They show how visible choice changes when one evidence-based constraint is applied; they do not tell you which constraint you should choose or predict hiring outcomes. The calculation uses the 200 most recent current roles from 1222 visible roles in this broad family.

Salary transparency context

Is pay disclosure typical for this kind of role?

This listing states usable structured compensation. About 19% of the bounded current role sample does the same.

This listingUsable pay stated

Compensation counts only when amount, ISO currency and supported pay period are all known.

Current same-role sample19%

Pay disclosure is uncommon in the current role sample.

Current employer pattern71%

This employer’s current visible listings disclose pay materially more often than the current same-role sample.

This compares salary-disclosure behavior in current visible listings. It does not compare raw salary amounts across currencies, infer undisclosed pay as zero, or rate employer quality.

Private offer & negotiation lab

Turn an offer into evidence, not guesswork

Enter an annual base offer and, if you want, your own target. HPJ compares only within one explicit currency and never chooses a target for you.

Advertised annualized payUSD 155,000–180,000 / yearListing evidence only.
Current comparable medianUSD 140,32920 current sampled roles
Current upper quartileUSD 171,363Current HPJ inventory context, not a salary recommendation.

Negotiation evidence available: advertised pay range, current same-currency market context, strong decision evidence.

Enter an annual base offer to compare it with explicit same-currency evidence. HPJ will not suggest a target for you.

Review the full package · 10 terms

Mark what is confirmed, what needs clarification and what you want to prioritize. These choices and notes stay in this browser.

Base payKnown

Usable advertised compensation is available.

Bonus / commissionMentioned

Variable compensation is mentioned; verify amount, formula, target and eligibility.

Equity / long-term incentivesMentioned

Equity or long-term incentive language is mentioned; verify grant type, vesting and valuation basis.

Retirement / pensionVerify

Retirement or pension benefits are mentioned; verify employer contribution and vesting.

Health benefitsMentioned

Health-benefit language is mentioned; verify coverage, dependants and employee cost.

Paid leaveMentioned

Paid-leave language is mentioned; verify the exact entitlement and carry-over rules.

Work arrangementKnown

A work-arrangement signal is available; verify schedule details before relying on it.

RelocationVerify

Relocation support not established

Visa / work authorizationVerify

Sponsorship not established

Review timingVerify

A review cycle is mentioned; verify timing and whether compensation can change at that review.

Questions worth taking into the offer conversation

  • What retirement or pension contribution does the employer provide?
  • If relocation is relevant, what expenses, timing and repayment conditions are covered?
  • What exact sponsorship or work-authorization conditions apply to this role?
  • When is the first performance and compensation review after joining?
  • Is there a firm application deadline, a rolling review window or no stated closing date?

Occupation intelligence

What broader career references connect to this role?

HPJ uses broad comparison families for current-market analysis. External taxonomy results are reference candidates for that broad family, not a declaration that this vacancy has been authoritatively classified into a specific occupation.

HPJ comparison familyData & AI roles
Adjacent families worth exploringSoftware engineering roles · Product roles

European occupation references

This service uses the ESCO classification of the European Commission.

U.S. occupation references

This site incorporates information from O*NET Web Services by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). O*NET® is a trademark of USDOL/ETA.

Career reference research

Skills worth investigating

Official occupational context, paired with exact phrases visible in this vacancy. It is not a skills test or an applicant-fit score.

ESCO · European occupation

data scientist

Usually essential in this ESCO occupation
  • statistics
  • think abstractly
  • apply research ethics and scientific integrity principles in research activities
  • integrate gender dimension in research
  • promote the participation of citizens in scientific and research activities
  • communicate with a non-scientific audience
  • implement data quality processes
Context-dependent ESCO skills
  • Hadoop
  • unstructured data
  • apply blended learning
  • teach in academic or vocational contexts
  • digital curation

O*NET · U.S. occupation

Data Scientists

O*NET occupation skills
  • Mathematics
  • Critical Thinking
  • Reading Comprehension
  • Complex Problem Solving
  • ProgrammingPhrase in listing
  • Active Learning
  • Speaking
  • Active Listening
Build my private skill preparation brief Your evidence · Practice · Interview questions

Plan how you might demonstrate or investigate occupational skills. These are broad-family reference examples, not confirmed vacancy requirements. Nothing you enter is sent to High Paid Job.

statisticsESCO · Essential · Listing mention unknown
think abstractlyESCO · Essential · Listing mention unknown
apply research ethics and scientific integrity principles in research activitiesESCO · Essential · Listing mention unknown
integrate gender dimension in researchESCO · Essential · Listing mention unknown
promote the participation of citizens in scientific and research activitiesESCO · Essential · Listing mention unknown
communicate with a non-scientific audienceESCO · Essential · Listing mention unknown
implement data quality processesESCO · Essential · Listing mention unknown
HadoopESCO · Optional · Listing mention unknown
unstructured dataESCO · Optional · Listing mention unknown
apply blended learningESCO · Optional · Listing mention unknown
teach in academic or vocational contextsESCO · Optional · Listing mention unknown
digital curationESCO · Optional · Listing mention unknown
MathematicsO*NET · Skills · Listing mention unknown
Critical ThinkingO*NET · Skills · Listing mention unknown
Reading ComprehensionO*NET · Skills · Listing mention unknown
Complex Problem SolvingO*NET · Skills · Listing mention unknown
ProgrammingO*NET · Skills · Exact phrase in listing
Active LearningO*NET · Skills · Listing mention unknown
SpeakingO*NET · Skills · Listing mention unknown
Active ListeningO*NET · Skills · Listing mention unknown

Private to this browser. Up to 20 reference skills; not a qualification assessment or hiring prediction.

Useful next question: Which of these occupational reference skills does the hiring manager actually expect for this specific role, and what evidence would demonstrate them?

These skills belong to one reference occupation selected for HPJ’s broad career family, not a verified classification of this vacancy. Text overlap only means a phrase appears in the available listing text; it does not establish an employer requirement, your proficiency, or a qualification gap. A missing mention is unknown, not a deficit.

Adjacent current roles to investigate

Product roles

Product Manager

Signal Processing Engineer (RF Processing & Analytics) · Paris

same seniority band · same employment type

Adjacent-family roles are exploration prompts from current HPJ inventory. They are not inferred qualification matches and they do not imply a promotion path.

Skill bridge

What does this role connect to?

Large Language Models is one of the signals this listing shares with skills appearing often across current Data & AI roles opportunities.

Skills named in this listing

Large Language ModelsMachine Learning

Shared with the current career sample

  • Large Language Models Seen in 34 sampled listings
  • Machine Learning Seen in 44 sampled listings

Application edge

  • If these skills match your background, prepare one concrete example that shows how you used Large Language Models and Machine Learning rather than relying on a keyword-only claim.
  • The listing mentions extra application steps. Review those requirements before starting so you can prepare the requested material in one pass.

If the named skills are genuinely transferable in your own experience, the occupation map also points to adjacent current families worth researching: Software engineering roles and Product roles. This is exploration context, not a qualification match.

Skill signals come from explicit text mentions. A mention is not automatically a requirement, and absence from the current top-skill list does not make a skill rare or unimportant. The current career comparison draws on 120 sampled listings; 118 contain a recognized skill signal.

Application Decision Lab

Turn the listing into a preparation plan

Separate what the listing actually states from what you still need to prepare or verify. Your selections and short notes stay in this browser and are never submitted to High Paid Job.

Listing mention

Large Language Models

This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.

Listing mention

Machine Learning

This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.

Application step

Prepare for a stated application step

An assessment, test or work-sample step is explicitly mentioned.

Questions worth resolving before you rely on the opportunity

  • Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?
  • Work authorization / sponsorship: If sponsorship or work authorization matters to you, what exact immigration or employer-sponsorship conditions apply to this role?

Decision analysis

What the listing really tells you

Useful decision context

The headline alone does not tell the whole story for Machine Learning Engineer - Inference Maintainer & Developer Experience at Roboflow. The clearest decision context comes from the advertised pay can be placed in context, the location rules are clear enough to judge, and the application route gives a few useful clues.

The advertised pay can be placed in context

Among 105 current comparable salaries in USD, the normalized annual midpoint is around the 50th percentile. This compares advertised pay in the same currency and does not include every element of total compensation.

The location rules are clear enough to judge

Location flexibility can be assessed here: Remote with some uncertainty or restrictions, with a Remote Freedom reading of 62/100. The most important location constraint is geographic eligibility is restricted to a country or region..

The application route gives a few useful clues

Provider-mediated application path · 1 visible process step. The external application may contain additional steps that are not visible from the listing itself.

Source continuity adds useful context

1 recent enabled source can currently renew this vacancy across 1 source classes. That improves traceability, although it does not verify the employer itself.

How this analysis is assembled

Built from the pay, location, qualification, mobility, application and source details available for this vacancy. Anything the listing leaves unresolved stays unresolved.

Analysis evidence coverage

How much analysis can the current evidence support?

100%Strong analysis evidence coverage
100%
Current-sample pay position50 / 100

Uses the explainable High Pay Score within the job’s current occupation/seniority/currency comparison sample.

Remote Freedom evidence62 / 100

Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.

Salary evidence qualityHigh confidence · Focused salary range

The configured source supplied structured salary fields, a usable currency and a focused comparison value.

Listing freshnessVery fresh

The listing was posted about 2 days ago.

Source provenance evidenceEstablished source evidence

At least one enabled source recently observed the vacancy. The source chain is traceable, but this is not independent employer verification.

There is no universal opportunity score because pay, flexibility and access matter differently to different people. Compare Jobs lets you weight the evidence around your own priorities.

Decision checklist

What is supported, uncertain or worth checking?

This checklist separates supported strengths, visible cautions and missing evidence. It is not an overall recommendation or guarantee that the job is suitable for you.

Supported strengths

No standout strength is supported strongly enough to list here. That is not a negative rating.

Visible cautions

  • Geographic eligibility is restricted to a country or region.
  • This is remote work with a geographic restriction, not a worldwide-remote vacancy.

Still unknown

  • Visa sponsorship is not established by the available listing data.

Qualifications & access

What qualifications are explicitly stated?

Entry-barrier evidence limited

EducationEducation requirement not established
Required experience evidenceExperience requirement not established
Explicit required barriers0
Explicit access signals0

Qualification signals come from explicit listing wording. Anything not mentioned remains unknown, so silence is never treated as proof that a degree, experience, licence or other requirement is unnecessary.

Pay vs. barriers

Pay and barrier context available

The current pay position can be viewed alongside the stated barriers, but the available detail does not support a stronger conclusion.

Current-sample pay position

Comparable disclosed-pay evidence places this job at about 50 / 100 within its current occupation/seniority sample.

Location-freedom constraints

1 explicit Remote Freedom restriction signal is present and is shown separately from qualification requirements.

Evidence gaps that could change this interpretation
  • Education requirement is not established.
  • Required prior-experience level is not established.

This section weighs pay context against clearly stated barriers without collapsing unlike requirements into one universal score. Degree, experience, licensing, clearance and location limits remain separate, and missing qualification details stay unknown.

Beyond the headline

Not enough evidence for a hidden-value finding

Potential hidden value appears only when several distinct non-pay advantages are actually supported. Missing requirements or benefits are never counted as positives.

Competitive current-sample pay position

The annualized salary midpoint is at or above about 50% of the current comparable disclosed-salary sample.

Still check: The current application path is provider-mediated rather than directly classified as an employer-authorized or official route.

Hidden Value is an experimental evidence summary, not an investment-style score or guarantee of employability. It only uses explicit current listing evidence and does not infer that an unmentioned qualification is unnecessary.

Explainable opportunity highlight

Why this opportunity stands out

These are supported signals from the current listing and comparison sample—not a guarantee that this is the best job for you.

Clear compensation evidence

The source provides structured salary information with a comparatively focused range, improving the usefulness of pay comparisons.

Fresh opportunity

The listing was posted about 2 days ago.

Compensation intelligence

How competitive is this pay?

50/100

Within the current market range

50%
Comparison familyData & AI roles
Comparable salaries105
Current-sample medianUSD 170,000
Middle 50% of sampleUSD 130,000–200,000
Position in sampleAt or above about 50%
Comparison geographySame country (US)
Pay positionAround the current median

The annualized midpoint is within about 2% of the current comparable median, so the difference is treated as effectively tied rather than meaningfully higher or lower.

Why this analysis?

  • This score reflects the current advertised-pay distribution for comparable active jobs in the same currency. It measures pay position, not overall job quality.
  • Salary confidence: High. The configured source supplied structured salary fields, a usable currency and a focused comparison value.
  • Range quality: Focused salary range.

Employer compensation communication

Pay transparency at Roboflow

Good current pay transparency

This score uses the employer’s current visible listings and combines how often pay is disclosed with how useful those ranges are. Bonus, equity and benefit mentions are shown separately and do not raise the score.

75/100
75%
Salary disclosure71% · 17 of 24 sampled listings
Range qualityMostly focused or specific pay information
Bonus wording22 current listings
Equity wording23 current listings
Benefits wording23 current listings
Evidence strengthStronger

This pattern comes from the employer’s current visible vacancies. It describes hiring and pay transparency in those listings, not employee satisfaction or a permanent judgement of the employer.

Current employer intelligence

Roboflow — current opportunity snapshot

Current opportunities visible24
Salary disclosure71%
Remote-policy evidence coverage100% · 24 of 24 sampled listings
Remote share of known arrangements100%
Worldwide-remote share0%
Known hiring countries1
Sponsorship evidence coverage0% · 0 of 24 sampled listings
Explicit sponsorship outcomes0 confirmed · 0 possible · 0 unavailable
Pattern sample24 of 24 · 100% coverage
Current role-family breadth9
Stated closing-date coverage0%
Relocation evidence coverage0% · 0 explicitly offered
Posted within 7 days46% of listings with a known posting date

Employer Evidence Profile

How often this employer’s current visible sampled vacancies state decision-critical information. These percentages describe evidence coverage, not employer quality.

Compensation71% of sampled current listings

Current sampled listings with advertised compensation evidence.

Work arrangement100% of sampled current listings

Current sampled listings whose work arrangement is clearer than unknown.

Location100% of sampled current listings

Current sampled listings with a supported country signal.

Application timing0% of sampled current listings

Current sampled listings with an explicit closing time.

Posting date100% of sampled current listings

Current sampled listings with a usable posting date.

Sponsorship0% of sampled current listings

Current sampled listings that explicitly indicate confirmed, possible or unavailable sponsorship evidence.

Relocation0% of sampled current listings

Current sampled listings with an explicit relocation signal.

Low coverage means the current visible listings state the information less often. It does not prove that the employer lacks the policy, benefit or support.

Common current occupation families: Sales (4) · Software engineering (3) · Customer success (3)

Visible seniority mix: Senior / lead (13) · Leadership (4)

Current employment-type mix: Full time (24)

Most visible hiring countries in this sample: United States (24)

Other current roles from this employer

These are other current vacancies visible from the same employer, not endorsements or evidence of future hiring plans.

Current disclosed salary medians by currency

  • USD 180,000 median · 17 disclosed salaries

Questions worth asking this employer

  • If work authorization matters to you, what sponsorship policy applies to this specific role and location?
  • If a move is required, is relocation assistance available for this position?
  • What is the expected application and interview timeline if the listing does not state a closing date?

These questions are prompted by missing or thin evidence in the employer’s current visible vacancies. Missing evidence is not treated as a negative judgement.

This employer snapshot uses the employer’s current visible listings. Remote and sponsorship rates use only vacancies with explicit evidence, while missing information stays unknown. Salary medians are shown separately by currency and need at least five disclosed salaries. Zero confirmed sponsorship means none was explicitly found in this sample, not that sponsorship is unavailable.

This snapshot describes the employer’s current visible vacancies—things such as pay disclosure and work arrangement. It is not an employee-satisfaction or hiring-quality rating, and different currencies stay separate.

Work-location intelligence

Remote Freedom

62/100

Remote with some uncertainty or restrictions

62%

Why this score?

  • The role is remote but the source identifies a country or regional restriction.

Location restrictions detected

  • Geographic eligibility is restricted to a country or region.

Remote Freedom measures observable location flexibility only. Unknown work-location information remains unscored. Timezone wording is penalized only when it appears restrictive; general collaboration across time zones is not treated as a restriction. A score does not guarantee legal work eligibility, payroll availability or visa sponsorship.

International applicant intelligence

Visa & relocation signals

Sponsorship status is shown only when the listing provides clear enough wording. Employer history or headquarters location is not used to guess an answer.

Visa sponsorshipSponsorship not established

The available listing details do not contain a clear enough sponsorship statement.

RelocationRelocation support not established

The available listing details do not contain a clear enough relocation statement.

Tradeoff check

What’s the catch?

These are visible conditions worth checking before treating the headline compensation as the whole story:

  • This is remote work with a geographic restriction, not a worldwide-remote vacancy.

Current alternatives

Better-paying alternatives

These current roles show a materially higher annualized salary midpoint in the same broad occupation and currency. Similar seniority, country, work arrangement, employment type and recency are preferred where possible; higher pay alone does not make an alternative a better fit.

About +34% midpoint

Machine Learning Engineer, Ranking & Retrieval

ClickUp · USA

Region-restricted remote

Why comparable: Same country · Similar work-location arrangement · Same employment type

Check: Seniority is not explicit in the alternative listing.

About +106% midpoint

Sr. Director of Machine Learning

hims & hers · USA

Region-restricted remote

Why comparable: Same country · Similar work-location arrangement · Same employment type

About +43% midpoint

Principal Data Engineer

hims & hers · USA

Region-restricted remote

Why comparable: Same country · Similar work-location arrangement · Same employment type

Alternatives are drawn from the current inventory rather than presented as application recommendations. A pay difference needs to be meaningful—about 3% or more at the midpoint—and different currencies are not compared.

Application-path intelligence

What can we see about the application process?

Provider-mediated application path

The current application path comes through a publisher or aggregator. The destination may still lead to an employer-authorized hiring system, but that is not clear enough to assume.

Current destination: jobicy.com · HTTPS

Visible application steps or requirements

  • An assessment, test or work-sample step is explicitly mentioned.

Application effort is estimated from visible listing and source details. Optional steps are kept separate from mandatory ones, and the external form may contain additional requirements that are not visible here.

Application safety context

Application safety check

Basic application-safety context available

The current application destination has a basic transport-safety signal such as HTTPS. This does not verify the employer, vacancy, recruiter or application process.

Evidence checked: Source provenance · Application destination · Listing wording

Observable transport-safety signals

  • The available application destination uses HTTPS.
Before you share money or sensitive information
  • Confirm the vacancy on the employer’s official careers site when practical, especially if a recruiter contacts you outside the listed application path.
  • Check that recruiter email domains and application destinations are consistent with the employer or an identifiable recruiting provider.
  • Do not pay application, processing, training or equipment fees merely to be considered for a job.
  • Do not send cryptocurrency, gift cards, online-banking credentials or PINs as part of an application.
  • Treat unusually fast offers or interviews conducted only through informal messaging channels as reasons to verify the employer independently.

This is a limited safety screen, not employer verification and not a fraud determination. Never send money, cryptocurrency, passwords or sensitive banking credentials merely because a listing appears on a job platform.

Original listing details

The section below comes from the listing source. The decision sections above use the job’s structured pay, location, qualification, mobility and application details to give you additional context.

Our mission is to make the world programmable. Sight is one of the key ways we understand the world, and soon this will be true for the software we use, too.

We’re building the tools, community, and resources needed to make the world programmable with artificial intelligence. Roboflow simplifies building and using computer vision models. Today, over 1M+ developers, including those from half the Fortune 100, use Roboflow’s machine learning open source and hosted tools. That includes counting cells to accelerate cancer research, improving construction site safety, digitizing floor plans, preserving coral reef populations, guiding drone flight, and much more.

Our team is small relative to our impact, and we believe our user success is our success (not the inverse). A team member summarized: “Roboflow is a company full of giant brains and tiny egos.” We find software has a multiplier effect on all roles (not only product and engineering), so Roboflow employs developers across the company in design, sales, customer support, marketing, and beyond.

We’re supported by great customers and investors, having raised over 63 million from Google Ventures, Y Combinator, Craft Ventures, Sam Altman, Lachy Groom, amongst other leading software investors.

At the center of all of this is inference — one of our most important open source projects and the engine that runs computer vision models everywhere, from cloud GPUs to edge devices in the field. It powers our commercial platform and is relied on by tens of thousands of developers. This role exists to be its steward.

Why This Role Exists

Inference is growing fast — and so is the volume of contributions, increasingly authored with the help of AI agents. That's a great problem to have, but it's outpacing our ability to keep quality high and cut releases on a predictable cadence. Today we ship roughly weekly, and it's a fight.

We want to flip that equation. The goal is to build and continuously evolve an agentic-driven contribution and release pipeline — automated and semi-automated review, triage, CI/CD, and end-to-end testing — so that we can safely absorb a high volume of agent-generated PRs while staying firmly in control of quality. The ideal end state: nightly end-to-end tests across every target (both standalone and on-platform), backed by a growing, world-grounded suite that validates the real health of every build. With that foundation, daily releases become routine, and we can say "yes" to far more contributions without ever lowering the bar — pushing back, by design, according to strictly defined review standards.

Alongside that, this person becomes the human face of inference: teaching internal teams and customers how to get more out of it, partnering with marketing to tell its story, and owning the (genuinely fun) work of bringing new models into the engine.


What We're Looking For

Primarily, you like to make great things with passionate colleagues. You are someone who likes to own outcomes, not only inputs. You're motivated by having responsibility and accountability. You're eager to 'do the work,' big and small.

You're motivated by the question, "How can I improve this?" and have a track record of doing so, even in ways adjacent to your role. Much of our current team is made up of former founders who thrive in the level of autonomy at Roboflow. Maybe you had a side hustle in high school or college.

You care about open source and the developers who depend on it. One of the best ways to stand out among other applicants is to write about something you've built with Roboflow, or to contribute to one of our open source projects — inference especially.

What You'll Do

  • Build and maintain inference, our flagship open source and commercial CV inference engine, keeping it healthy and high-quality as contribution volume scales.

  • Build an agentic-driven contribution pipeline — automated and semi-automated review, triage, and CI/CD — so we can safely accept a high volume of agent-generated PRs and move from weekly releases toward daily ones.

  • Design and grow a world-grounded, ever-expanding test suite that validates real build health across every target (standalone and on-platform), with the goal of nightly end-to-end runs across all of them.

  • Define and enforce the "rules of the road" — the review standards and skills that agents and contributors must follow. Exercise sharp judgment on when to merge fast and when to push back, and encode that judgment into the system itself.

  • Streamline how new models get added to inference (the most fun part of the job) — making it dramatically faster and easier to bring the latest computer vision and ML models to our users.

  • Teach and enable internal teams and customers. Keep our Field Engineers and Support team a step ahead so they can self-serve and go deeper, and help customers get the full value of the product.

  • Be the bridge between core engineering and clients — translating new capabilities into docs, demos, stories, and launches which would help people use inference more effectively.

  • Contribute to and grow the broader open source community around the project.

Who You Are

You are an experienced Machine Learning practitioner who wants to be an important part of an exceptional team that focuses on using Roboflow's computer vision tools to impact and improve every industry. You have high agency and a bias toward action.

  • 5+ years of hands-on experience building and operating production-grade ML systems, ideally involving large-scale deployment of modern AI models.

  • A real CV/ML foundation — you understand what inference does: how computer vision models work internally, how they're deployed across diverse environments, and how to adapt them for real-world, high-impact use.

  • Stellar agentic skills. You build with AI coding agents fluently and have a track record of using them not just to ship features, but to automate the engineering process itself — review, triage, testing, and CI. You have strong instincts for where agents excel and where they need guardrails.

  • Strong CS and systems background, with the ability to independently tackle complex programming, architecture, and reliability challenges and exercise sound judgment on when to move fast and when rigor is essential.

  • Hands-on experience with CI/CD, release engineering, and test infrastructure — you've built or substantially improved automated testing and delivery pipelines before.

  • Practical expertise with core ML technologies, including several of the following: PyTorch, TensorFlow, ONNX, TensorRT, vLLM (or other LLM/model deployment tools).

  • Strong proficiency in image and video processing, including several of the following: OpenCV, DeepStream, Pillow, PyAV, hardware-accelerated video decoding. Experience with video streaming protocols is an advantage.

  • Excellent communication and soft skills. You can teach, write clearly, and collaborate across engineering, support, field, and marketing — and you actually enjoy it. You're comfortable being a public-facing voice for a project.

  • Open source maintenance experience is a strong plus — you know what it takes to steward a busy repo and a community of contributors.

  • Level-up your performance with AI agents.

Where You'll Work

Roboflow is distributed across the US and Europe. We currently have Hubs in New York City and San Francisco (and plan to open more as we grow density in new cities). We provide opportunities (like team onsites in different cities) and resources (like a $4000/yr travel stipend) to work in person with other team members as much as you'd like, while also supporting remote team members. You can work from one of our Hubs (we offer a relocation bonus), work from home, work at co-working spaces, etc. We want you to work where you work best!

What You'll Receive

To determine your salary, we use a number of market and data-driven salary sources. We review all salaries every six months to ensure we stay in line with the market. This role has a range of $155K - $180K depending on level and location of candidate. We are open to paying beyond these ranges for exceptional talent. If this is you, please apply

We use Tier 1 rates for employees who work out of our San Francisco & New York hubs more than 3+ times per week.

In addition to our cash compensation, we offer generous perks and benefits. Below are some of the highlights:

  • $4000/yr Travel Stipend to travel anywhere anytime to work alongside other Roboflowers

  • $350/mo Productivity stipend to spend on things that make your work environment more productive, like high-speed internet at home or a co-working space

  • $350/mo AI Tools stipend

  • Cover up to 100% of your health insurance costs for you and your partner or family

  • $150/mo team lunch stipend

  • Remote first/flexible schedule allowing you to work collaboratively with other team members and asynchronously

  • Unlimited PTO- with an annual 2 week minimum, we encourage you to take time off for yourself

  • 12 weeks parental leave

  • Equity in the company so we are all invested in the future of computer vision

Interview Process (~5 hours)

Below is the interview process you can expect for this role.

Before the Interview:

  • We’ll review your application, LinkedIn, Github, etc.

  • The best way to stand out is to write about something you’ve built with Roboflow or contribute to one of our open source projects.

  • We may send you a technical screen if applicable.

Introduction Phase:

  • [15m] Technical Assessment

Team Interview Phase:

  • Live coding [45m]

  • Home assignment

  • [30m] Meet with Inference Core team member

  • [60m] Meet with hiring manager

    • Use this time to review specifics about the job description

    • Begin working through your 30/60/90 projects

    • Ask questions!

Final Interview Stage:

  • [45m] Meet with Head of Operations for a culture discussion

  • [30m] Meet with CEO

Note: you are welcome to request additional conversations with anyone you would like to meet and we will accommodate as best we can.

Not sure if this is you?

We want a diverse, global team with a broad range of experience and perspectives. If this job sounds great, but you’re not sure if you qualify, we encourage you to reach out to us at recruiting@robloflow.com or subscribe to our career newsletter by emailing "Subscribe" to operations@roboflow.com. We carefully consider every application and will either move forward with you, find another team that might be a better fit, keep in touch for future opportunities, or thank you for your time.

Learn More About Us

At Roboflow, we believe great ideas come from everywhere—and everyone. We’re proud to be an Equal Opportunity Employer committed to building a diverse and inclusive team. We consider all qualified applicants regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, veteran status, or any other legally protected characteristics.

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