ML Engineer (YC Start-Up) - Up to £180k + Equity
You'll work with Python, LLMs for post-training (RL, fine-tuning, distillation), distributed training, and inference optimisation. This YC-backed AI startup builds frontier systems modelling complex large-scale real-world behaviour. As a Research Engineer, you'll own the path from research to production, working directly with the CEO and Head of Research.
Free Tailor for ATS: 10/10 runs left
Few&Far are working with a small, fast-moving YC-backed AI startup that's building frontier systems modelling complex, large-scale real-world behaviour - genuinely hard from both a research and engineering standpoint, not an application layer bolted onto existing models. They're already working with some of the biggest organisations in the world and have already passed the 1M ARR milestone.
The role
They're looking for a Research Engineer to own the path from research to production. Their research team is inventing new methods; you'd take them from prototype to something running reliably at scale - post-training and distilling the models that power them, building the data pipelines that feed them, owning inference at scale, and simplifying aggressively so only what actually delivers performance makes it to production.
You'd work directly with the CEO and Head of Research, and partner closely with research, product, and forward-deployed engineering.
What they're looking for
2+ years shipping ML in a startup environment, ideally at an early-stage company where you owned things end-to-end.
Real hands-on post-training experience with LLMs - RL, fine-tuning, distillation, dataset and reward design
Strong, clean Python that other people can maintain
Comfortable reasoning about statistics, probability, and high-dimensional spaces
Fast, with a high bar - goes deep to understand both problem and solution
Genuine curiosity about the underlying domain, not just the ML
Ownership mentality - sees what needs to happen and makes it happen
Nice to have
Experience with distributed training, inference optimisation, or scaling ML systems
Background in an empirical field like behavioural science, computational social science, psychometrics, or statistics
Publications or open-source work in simulation, evaluation, calibration, or behaviour modelling
If you hit around 60% of this criteria, apply anyway - we'd rather see strong candidates who don't tick every box than miss out on good people over a checklist.