Job intelligence
Mid/Senior AI LLM Engineer (Full Remote - Netherlands)
EverAI
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.
Fast read
Opportunity DNA
This opportunity needs a closer read before drawing a strong conclusion, especially around the salary benchmark is still shallow.
The range is usable, but the comparable sample is still too shallow for a strong relative-pay call.
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
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
- The salary benchmark is still shallow: Pay is disclosed, but the current comparison pool is too small for a strong relative-pay conclusion.
Questions worth asking before you invest more time
- 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.
Salary transparency context
Is pay disclosure typical for this kind of role?
This listing states usable structured compensation, but the current role sample is still too small for a reliable disclosure-rate comparison.
Compensation counts only when amount, ISO currency and supported pay period are all known.
At least ten current comparable roles are required before HPJ publishes a disclosure percentage.
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: advertised pay range, 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.
Usable advertised compensation is available.
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
- 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?
- 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?
Skill bridge
What does this role connect to?
The listing contains useful skill signals, while the current occupation sample is still too thin for a broader comparison.
Skills named in this listing
Shared with the current career sample
The occupation sample is still too thin for a reliable shared-skill comparison.
Application edge
- If these skills match your background, prepare one concrete example that shows how you used Large Language Models and Python rather than relying on a keyword-only claim.
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.
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.
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.
Python
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
- 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?
- Application process: What interview, assessment, portfolio, reference or screening stages are part of the hiring process?
Decision analysis
What the listing really tells you
For Mid/Senior AI LLM Engineer (Full Remote - Netherlands) at EverAI, the most useful clues right now are pay is visible, but the benchmark is still thin, the location rules are clear enough to judge, and the application route gives a few useful clues. Looking at them together makes the trade-offs easier to see.
Pay is visible, but the benchmark is still thin
A usable EUR salary is available, but there are not enough comparable current observations for a strong percentile statement yet.
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 72/100.
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
This read combines the structured details currently available for pay, flexibility, qualifications, mobility, application effort and source continuity. Missing facts are left open for you to verify.
Analysis evidence coverage
How much analysis can the current evidence support?
This title does not yet fit a reliable comparison family, so a pay percentile is not shown.
Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.
The salary is source-provided and normalizable, but the advertised range is broad enough to reduce comparison precision.
The listing was posted about 3 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?
This title does not yet fit a reliable comparison family, so a pay percentile is not shown.
Why this analysis?
- This title does not yet fit a reliable comparison family, so a pay percentile is not shown.
- Salary confidence: Medium. The salary is source-provided and normalizable, but the advertised range is broad enough to reduce comparison precision.
- Range quality: Broad but usable range.
Employer compensation communication
Pay transparency at EverAI
Very strong 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.
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
EverAI — current opportunity snapshot
Employer Evidence Profile
How often this employer’s current visible sampled vacancies state decision-critical information. These percentages describe evidence coverage, not employer quality.
Current sampled listings with advertised compensation evidence.
Current sampled listings whose work arrangement is clearer than unknown.
Current sampled listings with a supported country signal.
Current sampled listings with an explicit closing time.
Current sampled listings with a usable posting date.
Current sampled listings that explicitly indicate confirmed, possible or unavailable sponsorship evidence.
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: Marketing (17) · Product (9) · Design (7)
Visible seniority mix: Senior / lead (22) · Leadership (70)
Current employment-type mix: Full time (89) · Contract (9)
Most visible hiring countries in this sample: Germany (16) · France (14) · United Kingdom (12) · Portugal (9)
Other current roles from this employer
Security Engineer (Remote - Germany)
Germany
Seniority not clearly specified · On-site / hybridMid/Senior AI LLM Engineer (Full Remote - France)
France
Senior / lead · On-site / hybridMid/Senior AI LLM Engineer (Full Remote - Germany)
Germany
Senior / lead · On-site / hybridMid/Senior AI LLM Engineer (Full Remote - Poland)
Poland, Portugal
Leadership · Remote — restrictions unclearThese are other current vacancies visible from the same employer, not endorsements or evidence of future hiring plans.
Current disclosed salary medians by currency
- EUR 85,000 median · 81 disclosed salaries
- USD 110,000 median · 10 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
Remote with some uncertainty or restrictions
Why this score?
- The role is remote, but the exact applicant-location restrictions are not clear.
Flexibility signals detected
- The description reinforces broad location independence.
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: jobicy.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.
Our Vision & Products
🚀 EverAI — Building the Future of AI Companionship
One of the Top 15 Largest & Fastest-Growing AI Companies in the World
50 Million Users in 2 years — Help Us Reach 100M first, 500M next
At EverAI, we’re shaping what it means to connect with AI. With 50 million users and counting, we're not just building products — we're creating entirely new categories.
Our flagship product is the world’s largest AI companionship platform, redefining relationships for millions. It is governed by our proprietary moderation system, EverGuard — an internal AI designed to ensure everything we build is safe, ethical, and human-first.
And we’re only just getting started!
Our Team
We are an enthusiastic, passionate and hardworking team of ≈ 100 people. Our founding team has strong entrepreneurial experience building and scaling web products from 0 to IPO.
Alexis Soulopoulos [CEO]
• 10+ years in Tech Executive Leadership
• Co-Founder Mad Paws Holdings (from 0 to IPO)
• Forbes 30 under 30 + Deloitte TechFast50 ’22 & ‘23
Michael Monin [Co-founder & CTO]
• 10+ years as CTO / COO (web2/web3), 3 years in AI/LLM
• Serial-entrepreneur: MTK Digital (exited / 0->$20m revenue) and Zipchat (AI Chatbot for E-commerce brands)
Thomas Lacroix [Co-founder & CMO]
• 8+ years in Customer Acquisition & E-commerce Growth
• Serial-entrepreneur: Curatible (sold to Blackstone) and MTK Digital (exited / 0->$20m revenue)
Maruša Fasano [CFO/Legal]
• 25+ years in Finance, Strategy, M&A
• Ex-CFO/M&A @Curatible (exited to Blackstone)
• Ex-President of the Board @SotremoSA (exited)
• Co-founder/CFO @SoftOne (exited)
Your Role
Most LLM roles stop at experimentation.
Here, the models you improve power experiences for 50M+ users.
Most of our traffic runs on self-hosted, fine-tuned open-weight models. On our internal roleplay evals, our smaller fine-tuned models can outperform much larger off-the-shelf models. Your job is to make them better.
You’ll join a small AI team led by Marco, our LLM Tech Lead, working closely with our CTO, Product, Web and DevOps teams. You’ll work across data, post-training, evals, and model behavior, taking ideas from failure analysis to production.
The problems are real: personalization, character consistency, long conversations, better evals, multilingual behavior, and faster training cycles.
If you want to shape model behavior at consumer scale, this is the role.
Key Responsibilities
-
Fine-tune open-weight LLMs using SFT, LoRA/QLoRA and post-training methods such as DPO and GRPO
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Diagnose model failures and turn them into experiments. Decide whether the answer is better data, post-training, prompting or classifiers
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Build training data and evals that directly shape what gets trained next
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Improve model behavior across personalization, consistency, tone, multilingual and group interactions
-
Ship to production and use real user behavior to guide the next iteration
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Speed up the training loop so experiments are faster, easier to compare, and more reproducible
Your Qualifications
-
You’ve personally post-trained open-weight LLMs for a user-facing product and shipped them to production
-
You’ve worked where AI was core to the product or business, not an experimental side feature
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Fine-tuning is core to your experience
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Strong Python, PyTorch and Hugging Face, with hands-on GPU training and debugging
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You build data and evals yourself and make decisions from evidence
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You can take work beyond notebooks into production
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You use AI tools daily in your own engineering work
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You are fully comfortable working with uncensored models and explicit NSFW content, including harmful material when developing and evaluating moderation system
Bonus points
-
Experience with conversational AI, roleplay products, or highly personalized consumer AI
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Multilingual LLMs
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Moderation, safety, or adversarial testing
-
Multimodal models
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Open-source LLM / post-training contributions
Why EverAI?
📈 Exponential Growth: From 50M users in 2 years, to 100M next — and 500M beyond
🚀 Track Record of Category-Creating Innovation: We consistently launch world-first AI applications — setting the pace, not following it
🌍 Global Impact: Top-tier user growth, real-world adoption, and cultural relevance
🧠 Proven Leadership: A senior team that’s launched, scaled, and exited & IPO’d multiple scale ups — now fully focused on reshaping AI companionship
👥 Elite Remote Team: 100% remote and built to win — world-class talent from Tier 1 tech companies, with a culture of ownership, velocity, and radical creativity
🛡️ Ethical Core: Our AI ecosystem is governed by EverGuard, our proprietary AI moderation technology, ensuring responsible development at scale
What We Offer
✍️ Contract Type: We prefer B2B, but we’re flexible, what matters is long-term commitment and impact
📍 Work From Anywhere: Fully remote. Choose the environment where you do your best work
🏝️ Paid Time Off: 4 weeks (20 working days) of PTO per year to recharge and reset
👨👩👧👦 Annual Gathering: A yearly in-person meetup to connect, brainstorm, and celebrate wins together
❤️🩹 Health & Wellness Support: Monthly allowance of 100 USD for health insurance expenses & access to unlimited 1:1 sessions with psychologists and lifestyle experts through OpenUp (also available to up to three of your family members)
🏢 Co-Working Space Budget: Work from a co-working space up to twice per month (35 EUR / 40 USD per visit) to stay inspired and connected
📚 Learning Budget: Dedicated funds to support your professional growth: courses, books, conferences, events, or certifications
💻 Company Laptop & Equipment: Laptop provided + monitor budget up to 250 USD + keyboard/mouse/mic/headphones budget up to 150 USD
⚡ AI Tools Access: Premium access to ChatGPT, Hugging Face, Claude Code, and any other tool needed to excel at your job, power your ideas and workflows
🎯 Top Tier Talent Is Our Multiplier
We’re a fully remote group of A-players from Tier 1 tech, led by an exec team who’ve launched, scaled, and exited multiple companies. We move fast, and care deeply about what we build — and who we build it with.
We’re looking for exceptional talent ready to ship & distribute world-first AI products at scale, fast, and co-create with us this category-defining business.
If that’s you — reach out and apply!
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 in the current source window
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: Jobicy. Destination: jobicy.com. The application opens in a new tab so you can keep this report open.