Job intelligence
Feltsense AI Engineer
Silver.dev
Before relying on the listing: Confirm Compensation and 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.
Fast read
Opportunity DNA
This opportunity needs a closer read before drawing a strong conclusion, especially around the pay picture is incomplete.
A financial comparison will need the approved compensation range.
The work arrangement can be assessed, although it is not among the most flexible patterns detected.
The listing does not make the minimum education or experience threshold clear enough yet.
Sponsorship and relocation support are not clear from the current listing.
Part of the application route can be assessed, but the full external process may contain additional steps.
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
2 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
- Work arrangement
- Advertised location
What to verify next
- Compensation: What is the approved compensation range, and what portion is guaranteed base pay rather than variable compensation?
- Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?
- Employment type: Is this permanent, fixed-term, contract, temporary, freelance or another employment arrangement, and which benefits apply?
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
- The pay picture is incomplete: No usable salary range is stated, so a meaningful financial comparison is not possible yet.
Questions worth asking before you invest more time
- Ask for the approved compensation range before comparing this opportunity financially.
- Confirm which countries employees may work from and whether payroll-country, timezone or office-attendance restrictions apply.
- 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
Huzzle · Germany
Why it is nearby: Similar work arrangement · Recently posted · 3 of 5 core decision facts stated
Different angle: Seniority is not explicit
Applied AI Engineer
Jobgether · Germany
Why it is nearby: Recently posted · 4 of 5 core decision facts stated
Different angle: Seniority is not explicit · Different work arrangement
Data Analyst
Avaron AB · Solna, Stockholms län, Sverige
Why it is nearby: Recently posted · 4 of 5 core decision facts stated
Different angle: Seniority is not explicit · Different work arrangement
Applied AI Engineer
iwoca · London
Why it is nearby: Recently posted · 3 of 5 core decision facts stated
Different angle: Seniority is not explicit · 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.
200 most recent current roles sampled for the disclosure measures below.
Distinct employers in the bounded current sample, not an estimate of all employers hiring in the market.
Current sampled roles stating at least four of the five Core Decision Facts.
Requires a numeric amount, explicit ISO currency and supported pay period.
Listings with explicit enough remote, hybrid or on-site evidence to classify.
Missing deadline evidence remains unknown and is not interpreted as an open-ended application window.
Currencies with explicit pay evidence in the sample: USD (39) · EUR (7) · GBP (3) · CHF (1). 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.
Counts only affirmative or possible sponsorship evidence, or relocation explicitly offered. Unknown is not treated as no.
A much stricter subset: only listings with explicit worldwide-remote evidence remain.
Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.
Shows the subset with enough stated evidence for a more informed first comparison.
Requires amount, explicit ISO currency and a supported pay period. Missing pay evidence remains unknown.
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 1255 visible roles in this broad family.
Salary transparency context
Is pay disclosure typical for this kind of role?
This listing does not state usable structured compensation. About 25% of the bounded current role sample does.
Compensation counts only when amount, ISO currency and supported pay period are all known.
Pay disclosure is limited in the current role sample.
Employer-level comparison is withheld until enough current employer listings are available.
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.
Negotiation evidence available: current same-currency market context.
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.
The approved base-pay range is not established in the listing.
Variable compensation is mentioned; verify amount, formula, target and eligibility.
Equity or long-term incentive language is mentioned; verify grant type, vesting and valuation basis.
Retirement or pension benefits are mentioned; verify employer contribution and vesting.
Health-benefit language is mentioned; verify coverage, dependants and employee cost.
Paid-leave language is mentioned; verify the exact entitlement and carry-over rules.
A work-arrangement signal is available; verify schedule details before relying on it.
Relocation support not established
Sponsorship not established
A review cycle is mentioned; verify timing and whether compensation can change at that review.
Questions worth taking into the offer conversation
- What is the approved base-pay range for this role?
- Is there a bonus, commission or other variable-pay component, and how is it calculated?
- Does the package include equity or another long-term incentive, and what are the vesting terms?
- What retirement or pension contribution does the employer provide?
- What health coverage is included and what costs remain with the employee?
- How much paid leave is provided and what are the carry-over or blackout rules?
- If relocation is relevant, what expenses, timing and repayment conditions are covered?
- What exact sponsorship or work-authorization conditions apply to this role?
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.
European occupation references
This service uses the ESCO classification of the European Commission.
U.S. occupation references
- Data Scientists 15-2051.00
- Computer and Information Research Scientists 15-1221.00
- Social Science Research Assistants 19-4061.00
- Atmospheric and Space Scientists 19-2021.00
- Bioinformatics Scientists 19-1029.01
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
- Programming
- 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.
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
STAGE Fullstack - Time-Travel debugger pour architecture événementielle (H/F)
WINAMAX · France
same seniority bandSTAGE Fullstack - Moteur de contrôle de règles et de conformité du SI (H/F)
WINAMAX · France
same seniority bandSTAGE Fullstack - Refonte d'un moteur de recherche à forte volumétrie (H/F)
WINAMAX · France
same seniority bandAdjacent-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?
Google Cloud is one of the signals this listing shares with skills appearing often across current Data & AI roles opportunities.
Skills named in this listing
Shared with the current career sample
- Google Cloud Seen in 25 sampled listings
- Large Language Models Seen in 35 sampled listings
Additional signals in this listing: Node.js · PostgreSQL · TypeScript
Application edge
- If these skills match your background, prepare one concrete example that shows how you used Google Cloud, Large Language Models, and Node.js rather than relying on a keyword-only claim.
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; 117 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.
Google Cloud
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.
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.
Node.js
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.
PostgreSQL
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.
TypeScript
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.
Questions worth resolving before you rely on the opportunity
- Compensation: What is the approved compensation range, and what portion is guaranteed base pay rather than variable compensation?
- Application closing time: Is there a firm application deadline, a rolling review window or no stated closing date?
- Employment type: Is this permanent, fixed-term, contract, temporary, freelance or another employment arrangement, and which benefits apply?
- Work authorization / sponsorship: If sponsorship or work authorization matters to you, what exact immigration or employer-sponsorship conditions apply to this role?
- Application process: What interview, assessment, portfolio, reference or screening stages are part of the hiring process?
Decision analysis
What the listing really tells you
Feltsense AI Engineer at Silver.dev becomes easier to judge once the location rules are clear enough to judge, the application route gives a few useful clues, and source continuity adds useful context are viewed together. Here is the current read from the information available.
The location rules are clear enough to judge
The work-location pattern is “Remote with some uncertainty or restrictions”, with Remote Freedom at 68/100.
The application route gives a few useful clues
Provider-mediated application path. 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
The brief pulls together the parts of the vacancy that can be compared reliably today. Gaps in the listing remain visible instead of being filled with assumptions.
Analysis evidence coverage
How much analysis can the current evidence support?
Comparable salary data is not available for this job yet.
Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.
Missing compensation is not treated as negative evidence; this dimension simply remains unavailable.
The listing was posted about 2 days ago.
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
No specific caution is supported by the currently available evidence. This does not prove that none exists.
Still unknown
- There is not enough comparable salary evidence to establish this job’s current pay position.
- Visa sponsorship is not established by the available listing data.
Pay vs. barriers
Opportunity Efficiency not assessed
The current pay position is not reliable enough to weigh compensation against the stated barriers.
- Current comparable pay position is unavailable.
- 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.
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.
Compensation intelligence
How competitive is this pay?
Comparable salary data is not available for this job yet.
Why this analysis?
- Comparable salary data is not available for this job yet.
- Salary confidence: Not available. No disclosed salary can currently be normalized for comparison. Missing compensation is not treated as low-quality salary evidence.
- Range quality: No disclosed range.
Employer compensation communication
Pay transparency at Silver.dev
Only 1 current listing from this employer is indexed. At least three are required before employer-level pay transparency is summarized.
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
Silver.dev — current opportunity snapshot
Only 1 current listing from this employer is visible. At least three are needed before an employer pattern is shown.
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
Remote with some uncertainty or restrictions
Why this score?
- The role is remote, but the exact applicant-location restrictions are not clear.
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.
The available listing details do not contain a clear enough sponsorship statement.
The available listing details do not contain a clear enough relocation statement.
Tradeoff check
What’s the catch?
No obvious trade-off appears in the available job details. That does not mean the role has no drawbacks, so the original listing and employment terms are still worth reading carefully.
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: remoteok.com · HTTPS
No additional required application step is clear in the available listing text. This does not mean the external application is one-click or friction-free.
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.
About the company
We raised $5.1M to build agents that operate as fully autonomous founders. Our backers include Matt Schlicht (founder of Moltbook), the founders of Crunchbase and Republic, and VCs including Draper, Precursor, and Liquid2.
We're working at the frontier of autonomous systems, building agents that don't just chat, but run entire businesses.
Our system identifies business opportunities through market signals, validates ideas with live ad tests, builds and launches software products, and brings in human experts wherever agents fall short. That loop already runs end to end: from idea to launched product to first revenue.
Over the next 12 months, our focus is on reliability, throughput, and self-improvement, making every launch better than the last.
About the role
We're looking for a hands-on AI Engineer to take ownership of the architecture and development of our autonomous agent system.
You'll lead the technical direction of the platform, building multi-agent orchestration systems, self-improving agent harnesses, and AI-powered backend services. This is a highly technical leadership role that combines architectural decision-making with hands-on engineering.
You'll be expected to ship production code, make key technical decisions, improve agent behavior in production, and help establish the engineering standards as the company scales.
Responsibilities
Own the system architecture and make key technical decisions across the platform.
Design and build multi-agent systems and orchestration infrastructure that take products from idea to launch.
Develop self-improving agent harnesses, including feedback loops, evaluation systems, and memory.
Build and maintain AI-powered backend services on top of LLM APIs such as Anthropic, OpenAI, and Gemini.
Write production code, particularly around agent runtime and orchestration.
Debug and improve AI behavior in production through prompt engineering, structured outputs, retries, and fallbacks.
Break down complex technical challenges into actionable milestones and prioritize the engineering roadmap.
Establish engineering standards around code reviews, testing, observability, and release processes.
Lead and support the engineering team while remaining deeply involved in implementation.
Requirements
4+ years of professional software engineering experience.
Previous technical leadership experience at an early-stage company (Founding Engineer, Tech Lead, Head of Engineering, or similar).
Hands-on experience building and shipping multi-agent systems and orchestration frameworks to production.
Experience developing self-improving or self-evolving agent harnesses.
Strong backend engineering skills, including designing and maintaining APIs, services, and data flows.
Experience working with LLM-based systems, including prompt engineering, model configuration, structured outputs, embeddings, retrieval, and evaluation.
Daily hands-on experience with AI coding agents such as Claude Code, Cursor, OpenAI Codex, or similar tools. You should know how to delegate work to agents, review their output, and verify what they build.
Nice to have
Strong TypeScript and Node.js experience.
Experience with Vercel AI SDK, Claude Agent SDK, or building custom agent runtimes.
Experience with PostgreSQL, Supabase, and pgvector.
Familiarity with background job systems such as Inngest or Pub/Sub.
Experience with Zod, Vitest, and GCP Cloud Run.
Experience growing or restructuring distributed engineering teams.
Interest in AI evaluation, safety, and observability.
Please mention the word **AMELIORATE** and tag RMjYwNzpmMjk4OjU6MTE3Yjo6YTg3Ojk5MjA= when applying to show you read the job post completely (#RMjYwNzpmMjk4OjU6MTE3Yjo6YTg3Ojk5MjA=). This is a beta feature to avoid spam applicants. Companies can search these words to find applicants that read this and see they're human.
Listing provenance
Where did this job come from?
See where this vacancy came from and whether more than one current source can still support it. Source names help with traceability and attribution; they are not endorsements or guarantees.
At least one enabled source recently observed the vacancy. The source chain is traceable, but this is not independent employer verification.
- Continuity
- One recent renewable source
- Source-class independence
- One renewable source class
- Last source observation
- Observed very recently
This records when High Paid Job last observed the vacancy in source evidence. It confirms source presence, not that the employer edited the vacancy at that time.
- 1 enabled source recently observed this vacancy.
Source confidence is about traceability and continuity. It is separate from application safety and should not be read as an employer reputation rating.
The vacancy is supplied by an external job publisher. Source information helps show where the listing came from; it is not a guarantee about the employer or vacancy.
Listing source: Remote OK. Destination: remoteOK.com. The application opens in a new tab so you can keep this report open.