Mid/Senior AI LLM Engineer (Full Remote - Europe)

EverAI

1d•Remote

About The Position

EverAI is hiring a mid- to senior-level AI LLM Engineer to improve open-weight language models powering its AI companionship platform, which serves more than 50 million users. Most of the company’s traffic runs on self-hosted, fine-tuned open-weight models, and smaller fine-tuned models have outperformed larger off-the-shelf models on internal roleplay evaluations. The role focuses on improving model behavior and taking work from failure analysis through to production. The engineer will join a small AI team led by the LLM Tech Lead and work closely with the CTO and Product, Web, and DevOps teams. The work spans data, post-training, evaluations, and model behavior, addressing challenges such as personalization, character consistency, long conversations, multilingual behavior, group interactions, and faster training cycles. The position is fully remote and open to working from anywhere. EverAI prefers B2B arrangements but is flexible, emphasizing long-term commitment and impact.

Requirements

Personally post-trained open-weight LLMs for a user-facing product and shipped them to production.
Experience working in a context where AI was core to the product or business, rather than an experimental side feature.
Fine-tuning is a core part of their experience.
Strong Python, PyTorch, and Hugging Face skills, including hands-on GPU training and debugging.
Ability to build data and evaluations and make evidence-based decisions.
Ability to take work beyond notebooks and into production.
Uses AI tools daily in engineering work.
Fully comfortable working with uncensored models and explicit NSFW content, including harmful material when developing and evaluating moderation systems.

Nice To Haves

Experience with conversational AI, roleplay products, or highly personalized consumer AI.
Experience with multilingual LLMs.
Experience with moderation, safety, or adversarial testing.
Experience with multimodal models.
Open-source LLM or post-training contributions.

Responsibilities

Fine-tune open-weight LLMs using SFT, LoRA/QLoRA, and post-training methods such as DPO and GRPO.
Diagnose model failures and turn findings into experiments.
Determine whether model improvements require better data, post-training, prompting, or classifiers.
Build training data and evaluations that inform subsequent training decisions.
Improve model behavior in personalization, consistency, tone, multilingual use, and group interactions.
Ship improvements to production and use real user behavior to guide further iterations.
Improve the training loop so experiments are faster, easier to compare, and more reproducible.

Benefits

Four weeks (20 working days) of paid time off per year.
Annual in-person team gathering.
Monthly allowance of 100 USD for health insurance expenses.
Unlimited one-to-one sessions with psychologists and lifestyle experts through OpenUp, also available to up to three family members.
Co-working space budget for up to two visits per month, at 35 EUR / 40 USD per visit.
Learning budget for courses, books, conferences, events, or certifications.
Company laptop provided.
Monitor budget up to 250 USD.
Keyboard, mouse, microphone, and headphones budget up to 150 USD.
Premium access to ChatGPT, Hugging Face, Claude Code, and other tools needed for the job.