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Mid/Senior AI LLM Engineer (Full Remote – Netherlands)

AI Summary
EverAI is seeking a mid-to-senior LLM engineer to improve self-hosted, fine-tuned open-weight models used by a large consumer AI companionship platform. The role covers the full post-training loop: diagnosing failures, creating datasets and evaluations, fine-tuning with SFT, LoRA/QLoRA, DPO and GRPO, and shipping improvements to production. The engineer will work closely with AI, product, web and DevOps colleagues on personalization, character consistency, multilingual behavior and long-context conversations. This is a high-ownership remote position for someone with proven production experience training and deploying LLMs. Comfort working with explicit NSFW and potentially harmful content for moderation development and evaluation is required.
Remote from
Netherlands, Portugal
Salary
EUR 75k–125k / yr
Employment
Full Time
Experience
Director
Published
Apply before
7 Dec 2026
Listing views
35
Application actions
3
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Opportunity details

About this role.

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

  • Diagnose model failures and turn them into experiments. Decide whether the answer is better data, post-training, prompting or classifiers

  • Build training data and evals that directly shape what gets trained next

  • Improve model behavior across personalization, consistency, tone, multilingual and group interactions

  • Ship to production and use real user behavior to guide the next iteration

  • 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

  • Fine-tuning is core to your experience

  • Strong Python, PyTorch and Hugging Face, with hands-on GPU training and debugging

  • You build data and evals yourself and make decisions from evidence

  • You can take work beyond notebooks into production

  • You use AI tools daily in your own engineering work

  • 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

  • Multilingual LLMs

  • Moderation, safety, or adversarial testing

  • Multimodal models

  • 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!

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe position requires demonstrated production post-training expertise, GPU debugging, evaluation design and evidence-based decisions across data, prompting and model methods. Work affects a large-scale consumer product and demands rapid, reproducible experimentation.

Salary analysis

Estimated compensation compared with the broader EU market for similar roles.

Estimated job median (per year)Market rate
€100,000
EU market range (per year)€80k–€135k
AI insightThe employer discloses a base compensation range of €75,000–€125,000 per year, with a midpoint of €100,000. Estimated European market compensation for a mid-to-senior production LLM engineer is approximately €80,000–€135,000 per year; these are AI estimates, not verified salary research.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an open-weight LLM you post-trained and deployed to a user-facing product. What changed after deployment?

I would explain the model, the starting failure modes, the post-training approach, evaluation criteria, deployment process, and measured changes in user-facing quality, safety, latency, or engagement.

How do you decide whether an LLM failure should be addressed through data, prompting, fine-tuning, or a classifier?

I first cluster and quantify failures using targeted evaluations and production examples. I use prompting for controllable presentation issues, data or post-training for repeated behavioral gaps, and classifiers or policy layers when deterministic safety enforcement is needed.

How would you build an evaluation suite for character consistency and long conversations?

I would define behavioral rubrics, create multi-turn scenarios with controlled facts and contradictory prompts, include adversarial cases, and track automated and human-scored metrics by failure category across every model iteration.

What practices make GPU training experiments reproducible and fast to compare?

I would version datasets, prompts, code, model checkpoints, and configuration files; log seeds and hardware details; standardize evaluation gates; and use experiment tracking to compare quality, cost, throughput, and regressions.

How would you approach training and evaluating models that may encounter explicit or harmful content?

I would use tightly governed datasets and access controls, establish clear labeling and safety taxonomies, separate allowed from disallowed behaviors, conduct adversarial evaluations, and validate moderation safeguards before release.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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