About Us
At OVHai LLM, we build large language models and design platforms and services around these models for specific use cases. We’re a tight-knit team of 15 researchers and engineers, united by curiosity and a shared drive to push the boundaries of what LLMs can do — from training and fine-tuning to rigorous evaluation and benchmarking.
Our team is genuinely diverse and multicultural, bringing together different backgrounds, perspectives, and ways of thinking. That mix is core to how we work: we believe the best research comes from teams that challenge each other’s assumptions and learn from one another every day.
We take our work seriously, but not ourselves. Expect a collaborative, low-ego environment, plenty of interesting debates, shared lunches, and lots of fun — alongside real opportunities to contribute to cutting-edge LLM research from day one.
The role
This is a senior-level position for someone who has taken part in training LLMs end-to-end and wants to play a leading role in how our future models are built. You will work alongside the rest of the research team and are expected to have deep hands-on experience with at least one stage of large-scale training, strong opinions about what makes a training run succeed or fail, and the ability to turn a paper into a working experiment on a multi-node cluster.
Depending on your background and where the roadmap needs you, you might focus on one of the following: the core pretraining recipe, post-training and reinforcement learning, data, evaluation, or extending the models to new modalities.
What you will do
- Design, launch and monitor training runs on multi-node GPU clusters (EuroHPC systems and OVHcloud infrastructure), from architecture experiments to full-scale runs of several billion parameters on trillions of tokens.
- Improve and maintain our training framework: parallelism strategies, memory, throughput, checkpointing, stability and reproducibility.
- Contribute to the recipe at every stage: pretraining data mixture and curriculum, mid-training, SFT, preference optimisation and RL with verifiable rewards.
- Build and extend the data pipelines that feed these runs: sourcing, filtering, deduplication, synthetic data generation, quality scoring, tokenisation.
- Design evaluations that actually predict downstream usefulness, including domain-specific benchmarks, and make sure they run continuously.
- Work on our current in-house LLM architectures and design future ones.
- Mentor junior engineers and interns.
What we look for
- Several years of hands-on experience training or fine-tuning large neural networks, including at least one project on models above ~3B parameters.
- Deep fluency in PyTorch and the modern LLM stack (Hugging Face ecosystem, FSDP/DeepSpeed/Megatron-style).
- Comfortable debugging the full stack: data loaders, NCCL and networking, numerical instabilities, loss spikes.
- Strong software engineering habits: tests, clean abstractions, code review, reproducible experiments.
- Ability to read a recent paper, judge whether it matters for us, and implement it.
- Fluent in both English and French (written and spoken).
Nice to have
- Post-training experience: SFT/DPO/GRPO-style RL, reward modelling, RL environments and verifiers.
- Pretraining data work at scale: web crawls, dedup, quality classifiers, tokeniser training, data mixing.
- Low-precision training and inference and hands-on experience with FP8.
- Evaluation and benchmarking infrastructure (lm-eval-harness or in-house), including judge-based evals.
- A good understanding of Hybrid or state-space architectures (Mamba, GDN, Hymba-style designs) and long-context methods.
- Inference and serving (vLLM, SGLang, TensorRT-LLM, custom kernels, Triton/CUDA).
- Experience with multimodal or document-understanding models.
- Slurm and Kubernetes; experience on HPC centres (EuroHPC, GENCI/Jean Zay, etc.).
- Publications at top venues, open-source contributions, or public model releases.
The Recruitment Process
- A 15-minute introductory call with a senior researcher to discuss the role
- An interview and technical use case assessment with two members of our research team
- An HR interview to assess soft skills
- Debriefing session
How to Apply
Interested? Send your CV, a short motivation letter, and a link to your LinkedIn profile to hr@llm.ovhai.com — please include the reference (R-002 – Senior Research Engineer, LLM) in your subject line so we can route it to the right team quickly.