Member of Technical Staff - AI Engineer (up to £240k)

Dex; Greater London; Hybrid; GBP 90,000 - 150,000; Full time

Job summary

Dex is partnering with Frontier labs to recruit a Member of Technical Staff in London or Paris. You will own ambiguous research-engineering problems across data curation, model fine-tuning, and agent execution harnesses, applying SFT and RL and turning prototypes into hardened systems.

You will design training-data pipelines, fine-tune/post-train models, develop agent harnesses, and collaborate with software and curriculum teams to translate open-ended questions into production-ready solutions.

Qualifications

• Hands-on experience fine-tuning or post-training models using SFT or RL.
• Strong grounding in AI/ML fundamentals beyond orchestration.
• Proficiency in Python and building production-grade code.
• Ability to translate ambiguous research questions into technical plans.
• Based in or willing to work hybrid from London or Paris.

Responsibilities

• Design, curate, and scale training-data and synthetic-data pipelines for agent training.
• Fine-tune and post-train models using supervised fine-tuning and RL methods.
• Develop and harden agent harnesses and sandboxed execution environments.
• Translate open-ended AI questions into technical plans and production-ready systems.
• Drive technical decisions and coordinate complex engineering efforts across the team.

Skills

Fine-tuning / post-training models

Python programming

AI/ML fundamentals

System design & evaluation

Hybrid work readiness London/Paris

Tools

PyTorch

TensorFlow

RLHF

Job description

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.

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.