Member of Technical Staff - AI EngineerComp confirmed on your call.

The role

Frontier labs do not lack compute; they lack high-signal data and rich, resettable digital environments where agents can learn complex tasks through trial and error. Backed by prominent seed investors and led by a repeat founder with a prior exit, this European AI lab scaled to more than 50 staff across London and Paris in roughly a year, building the foundational environment and data layer currently relied on by leading frontier AI labs.

As a Member of Technical Staff, you will take ownership of ambiguous research-engineering problems across data curation, model fine-tuning, and agent execution harnesses. This is not an application-layer role stitching third-party APIs together, nor is it an academic seat divorced from production; you will apply methods like SFT and RL, measure what holds up under rigorous evaluation, and turn viable prototypes into observable, hardened systems.

The work

• Design, curate, and scale training-data and synthetic-data pipelines purpose-built for agent training across complex digital workflows.
• Fine-tune and post-train models using supervised fine-tuning and reinforcement learning methods, establishing rigorous feedback loops and evaluation suites.
• Develop and harden agent harnesses and sandboxed execution environments that reliably connect models to real, interactive software tasks.
• Translate open-ended, ambiguous AI questions into technical plans and production-ready systems alongside software and curriculum engineering teams.
• Drive technical decisions and coordinate complex engineering efforts across the team without relying on line-management hierarchy.

What you bring

• Direct, hands-on experience fine-tuning or post-training models using methods like SFT or RL, with the ability to detail precisely what was trained, how it was evaluated, and what changed.
• Deep technical grounding in AI and ML fundamentals beyond application-layer orchestration, specifically around model optimisation, RLHF, and rigorous evaluation design.
• Strong Python foundations and software engineering discipline, writing clean, testable, production-grade code rather than isolated research prototypes.
• Proven ability to break down ambiguous research-engineering questions from first principles and drive complex technical initiatives over multi-month horizons.
• Based in or willing to work hybrid from London or Paris.