Member of Technical Staff - AI Engineer

This role is with one of Dex's trusted partner companies. We work closely with their teams to truly understand their culture, goals, and what they're looking for, so we can match you with the right opportunity and give you context about the role before you commit to a process.

Frontier AI systems have hit the limits of static web scrapes and now require active, trial-and-error experience on real software to improve. Operating out of engineering hubs in London and Paris and collaborating closely with the world's leading AI labs, this team builds resettable digital environments where autonomous agents navigate real-world software, fail, learn, and generate the experiential datasets powering the next generation of foundation models.

You will join as a Member of Technical Staff owning end-to-end research engineering across model post‑training, evaluation loops, and sandbox execution systems. This is a hands‑on systems and model engineering seat, not a prompt‑wrapper role and not an ivory‑tower research track. You will turn open‑ended questions into working technical architectures, run training and fine‑tuning pipelines, and ship production‑grade infrastructure that directly expands what frontier models can do.

• Fine‑tune and post‑train models using SFT, reinforcement learning, and targeted feedback loops, moving far past public API integration into direct model adaptation.
• Build and scale data creation and curation pipelines that extract high‑signal synthetic and experiential training data from agent task trajectories.
• Design evaluation harnesses and automated benchmark environments that measure agent performance, task completion, and regression across complex digital tasks.
• Develop resilient agent harnesses and sandboxed execution environments that reliably interface models with real desktop, web, and enterprise applications.
• Prototype novel training and agent workflows rapidly, isolate what holds up against real task distributions, and product ionise winning approaches alongside software engineers.

• Direct, hands‑on experience training, fine‑tuning, or post‑training models with methods like SFT, RL, or RLHF, with deep knowledge of what changed and why.
• Strong software engineering fundamentals in Python, producing clean, tested, and observable production‑quality systems rather than disposable research scripts.
• Proven track record of breaking down ambiguous AI problems from first principles and driving substantial technical initiatives across multi‑month horizons.
• Demonstrated technical leadership without formal management authority, setting technical direction and coordinating complex projects across peer engineers.
• Ability to work on‑site from engineering hubs in London or Paris.

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