Research Scientist Reinforcement Learning (Foundation Models)

The role

We’re hiring a Research Scientist (Reinforcement Learning) to help bring RL into the core of how foundation models are adapted and improved for industrial use.

In industry, models don’t live in isolation: domain experts validate, correct, and act on model outputs. We want RL to leverage that expert feedback not only as a post‑training patch, but increasingly earlier in the pipeline, shaping objectives, training signals, and adaptation strategies.

What you’ll work on

Human‑in‑the‑loop reinforcement learning

• Turn expert validation/correction into a reliable learning signal.

• Design feedback interfaces/signals that are practical in real operational settings.

RL for industrial foundation models

• Develop RL methods that sit on top of (or integrate with) foundation models used in production.

• Explore ways for RL to intervene earlier in the chain (not just after deployment).

From research to deployment

• Build evaluation protocols aligned with real constraints: robustness, uncertainty reduction, safety, auditability, and cost of error.

• Work closely with scientists/engineers to ship demonstrators that connect benchmarks to field outcomes.

What we’re looking for

• PhD (preferred) or equivalent research experience in Reinforcement Learning / Machine Learning.

• Strong foundations in RL (e.g., policy optimization, off‑policy learning, offline RL, exploration, credit assignment).

• Ability to design rigorous experiments, debug failure modes, and iterate fast with scientific discipline.

• Strong programming skills (Python; deep learning stack such as PyTorch).

Nice to have

• Experience with real‑world RL constraints (noisy/limited feedback, safety requirements, deployment considerations).

• Comfort with complex data modalities (time series, scientific/industrial signals, multimodal setups).

• Publications or open research artifacts in RL / sequential decision‑making.

Why join

• Work on RL problems that matter in the real world: expert feedback loops, uncertainty reduction, and mission‑critical constraints.

• A research culture that values clarity, rigor, and humility and that connects fundamental ideas to deployable systems.

• High ownership in a small team: you’ll shape direction, not just execute tasks.

• Recruitment prescreen (30-45min)

• Scientific deep dive (remote-45min)

• Half day of scientific interview (Architecture - Coding - Research talk) + Culture fit

• References call