Post-Training Research Scientist (LLMs) — Experimental Track

This role sits at the heart of Vetto's mission: using high-quality human data to build AI systems that make the world better. You'll take raw expert signals and turn it into tangible model improvement, experimenting rapidly and carving new paths in post-training. With full autonomy and no production constraints, you'll have the freedom to try unconventional ideas and see their impact quickly.

• Design and run post-training experiments on frontier and open-weight LLMs (SFT, preference-based methods, rubric-driven training)
• Translate raw annotation artifacts (multi-step solutions, evaluations, adversarial prompts) into training-ready datasets.
• Prototype new reward signals beyond pairwise preferences (rubrics, constraints, structured critics).
• Analyze failure modes; propose data-centric fixes (sampling, curriculum, counterfactuals).
• Build lightweight training/eval pipelines; iterate quickly.
• Produce short internal memos: what worked, what didn't, why.

We operate remote-first. We focus on outcomes, not where the work is done. To support flexibility and personal choice, we maintain offices in select locations as an optional resource for the team.

Location: Flexible (EU-friendly time zones preferred)

Type: Full-time or long-term contract

We're looking for a researcher who thrives with autonomy, is hands-on, and brings a strong execution mindset and startup mentality. You are opinionated about data quality, pragmatic about tradeoffs, and comfortable moving quickly with incomplete information. You have strong experimental instincts - you can design, run, and interpret messy experiments and extract meaningful insights from them.