Mid​/Senior AI LLM Engineer; Full Remote - Europe

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, Ever Guard — an internal AI designed to ensure everything we build is safe, ethical, and human-first
.

And we’re only just getting started!

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 Tech Fast
50 ’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 @Soft One (exited)

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 Dev Ops 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.

• 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

• 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

• Experience with conversational AI, roleplay products, or highly personalized consumer AI
• Multilingual LLMs
• Moderation, safety, or…