Lead RL Research Engineer — Real-World, Long-Horizon

FBED Labs.; Greater London; Hybrid; GBP 108,000 - 132,000; Full time

Benefits offered by this job

Bonuses

Equity

Job summary

FBED Labs in London is seeking a leading research engineer to drive long-horizon reinforcement learning research in production-facing environments. You will design, train and evaluate models, working with real sequential data and logging policies to ensure robust training epochs.

You will collaborate with the CTO/CEO and an ML platform engineer, owning research directions and partnering with lab partners for rigorous evaluations and memory-focused investigations.

Qualifications

• Hands-on experience in reinforcement learning or sequential decision modelling.
• Proven track record in long-horizon credit assignment.
• Working knowledge of causal inference and causal graphical models.
• Strong experience training and evaluating models (PyTorch or similar).
• Experience designing environments, benchmarks or evaluations.

Responsibilities

• Build long-horizon environments and task suites from real sequential data.
• Design state representations that stay sufficient across long, partially observed trajectories.
• Train, post-train and evaluate our models, from parsing models to Shannon Zero.
• Run rigorous evaluations against human raters and frontier models.
• Own the logging policy and labelling targets to ensure enough coverage per decision point.
• Run QA on environments delivered to lab partners and be their technical counterpart.
• Explore memory-related problems: what to keep, what to consolidate and what to forget.
• Lead a second research engineer, working with the CTO and CEO and alongside an ML platform engineer.

Skills

Reinforcement learning

Sequential decision modelling

Off-policy evaluation

Causal inference

PyTorch

Environment design

Data wrangling in real-world data

Tools

PyTorch

Job description

FBED Labs in London is seeking a leading research engineer to drive long-horizon reinforcement learning research in production-facing environments. You will design, train and evaluate models, working with real sequential data and logging policies to ensure robust training epochs.

You will collaborate with the CTO/CEO and an ML platform engineer, owning research directions and partnering with lab partners for rigorous evaluations and memory-focused investigations.