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

AI Summary
EverAI is hiring a mid/senior LLM engineer to improve self-hosted, open-weight models powering a large consumer AI companionship platform. The role covers fine-tuning, post-training, training-data development, evaluation design, model-failure analysis, and production deployment. The engineer will work with Python, PyTorch, Hugging Face, GPU infrastructure, and methods including SFT, LoRA/QLoRA, DPO, and GRPO. Success depends on rapidly converting user and evaluation signals into reproducible model improvements for personalization, consistency, multilingual interactions, and safety-related behavior.
Remote from
France, Portugal
Salary
EUR 75k–125k / yr
Employment
Full Time
Experience
Director
Published
Apply before
7 Dec 2026
Listing views
19
Application actions
0
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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 insightThis is a senior production ML role requiring direct experience post-training open-weight LLMs and deploying them in user-facing systems. It combines advanced model-training expertise with experimentation, data design, evaluation, debugging, and fast product iteration at substantial scale.

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–€130k
AI insightThe employer discloses an annual base compensation range of €75,000–€125,000, with a midpoint of €100,000. Estimated European annual market range for a mid/senior production LLM engineer is €80,000–€130,000; actual offers may vary by location, contract structure, and demonstrated post-training experience.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an open-weight LLM post-training project you took from data preparation to production.

I would explain the user problem, base model selection, data collection and quality controls, training approach, evaluation criteria, deployment process, and the measurable product or model-quality outcomes. I would also describe what failed during experimentation and how those results changed the next iteration.

How do you decide whether a model failure requires better data, prompting, fine-tuning, or a classifier?

I first categorize failures using targeted evaluation slices and inspect representative examples. I use prompting for instruction or context issues, data improvements when examples are missing or inconsistent, fine-tuning when the desired behavior must generalize reliably, and classifiers or guardrails when deterministic policy enforcement is needed.

How would you evaluate character consistency over long AI conversations?

I would define behavioral and factual consistency criteria, build multi-turn test scenarios with known persona facts and adversarial contradictions, and score results through automated checks plus calibrated human review. I would track performance by conversation length, language, persona type, and regression against prior releases.

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

I would version datasets, prompts, model checkpoints, code, environment configuration, and hyperparameters; log metrics and artifacts centrally; and use fixed evaluation suites. I would also automate launch pipelines, establish comparable baselines, and prioritize small diagnostic runs before expensive full training jobs.

How would you approach safety evaluation when working with uncensored models and explicit content?

I would use controlled access, clear handling procedures, carefully defined policy taxonomies, and segmented adversarial evaluation datasets. I would measure both unsafe-output rates and over-refusal, document failure modes, and combine model-level improvements with moderation classifiers and monitoring 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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