Senior AI Engineer
Pubblicato il 09-09-2026 - Principled Intelligence in Frosinone
Fully remote, with an office in the center of Rome you are welcome in as often or as rarely as suits you
Esperienza, qualifiche e soft skill: possiede tutti i requisiti per avere successo in questa possibilità? Lo scopra qui sotto.
We are hiring a Senior AI Engineer to train the next generation of our models, including the small language models that carry most of the load in production. Anyone who has taken a general-purpose model into production has reached the point where the remaining improvements stop coming from prompts and context and start coming from training: from data curation to continual pre-training, post-training with reinforcement learning, model merging, and model evaluation. This role owns that work end to end. We firmly believe the next move in the state of the art will not necessarily come from whoever has the most compute, and we would like to be one of the places it comes from instead.
What you'll do
- Train, fine-tune and distil new model families, from dataset construction through to the evaluations that decide whether a run ships.
- Build the training and evaluation infrastructure that makes results reproducible, including harnesses that catch regressions in behaviour a benchmark score will not surface.
- Curate and generate training data, and decide what a given capability actually requires: more data, better data, a different teacher model, or a change in objective.
- Take models the last distance into production, working with the engineers who serve them on quantisation, latency and the cost per answer at volume.
- Choose which questions are worth pursuing,
since several of the ones in front of us have no settled answer in the literature yet.
Minimum qualifications
- Five or more years of engineering experience, with at least one spent training or fine-tuning language models.
- Fluency in PyTorch and experience with distributed training across multiple GPUs or nodes.
- Practical experience with post-training methods, including supervised fine-tuning and at least one preference optimisation approach such as DPO.
- Experience designing evaluations for your own models, rather than just reporting numbers from a benchmark.
- A record of having found and fixed the causes of a model behaving badly, including the cases where the cause turned out to be the data.
Preferred qualifications
- Experience with knowledge distillation into models small enough to serve under a fixed latency budget.
- Pretraining experience, at any scale.
- Work on inference performance: quantisation, speculative decoding, serving with vLLM or similar.
- Kernel-level work in Triton or CUDA.
- Published research, open source contributions, or models other people have used.
What we offer
- Competitive salary (8 USD; 7 EUR).
- Stock options.
- Standard benefits package.
- Fully remote by default.
- The office is there for people who prefer it, and presence is welcome without being counted. The office is in the city center of Rome (inside Termini Station) with direct access to subways (Line A and Line B), trains and buses. xlwpduy
If you have trained models that other people then depended on, we would like to hear about what broke and what you changed.