Research Engineer (Post-Training)

Axiōma Search; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Stealth AI Lab in London or Paris (Hybrid) is seeking a Member of Technical Staff – Post-training / RL to improve models after their initial training. You will advance reinforcement learning and post-training methods while strengthening the systems that run large-scale experiments.

You’ll work across both learning algorithms and the infrastructure, from RL experiments to GPU profiling, identifying bottlenecks and ensuring system changes do not alter how the model learns.

Qualifications

• Strong programming and quantitative problem-solving skills.
• Hands-on experience with PyTorch and model training.
• Understanding of reinforcement learning and/or post-training for LLMs.
• Experience with distributed training and GPU systems.
• Strong experimental judgement.
• Ability to work across ML research and systems engineering.

Responsibilities

• Build and improve supervised fine-tuning, preference optimisation and RL methods.
• Work with approaches including PPO, GRPO and SDPO.
• Own training loops from rollout generation through to policy updates and checkpointing.
• Improve training throughput, GPU utilisation and memory efficiency; profile and fix bottlenecks across distributed training.
• Investigate instability and differences between training and inference.
• Use real model failures to improve rewards, training data and overall performance.

Skills

Strong programming

Quantitative skills

Experimental judgement

ML research collaboration

Tools

PyTorch

Distributed training

GPU systems

Job description

Member of Technical Staff – Post-training / RL

Stealth AI Lab | London or Paris (Hybrid)

About

This role is about making models better after their initial training. You'll work on reinforcement learning and other post-training methods, while also improving the systems needed to run those experiments efficiently at scale.

This startup is building AI systems that learn how to carry out complex work inside large organisations. They recreate real-world workflows as interactive training environments, then use those environments to train models through practice and feedback — so the models get better at completing long, multi-step tasks reliably, rather than simply generating answers.

You'll work across both the learning algorithms and the infrastructure underneath them. That means going from an RL experiment to a GPU profiler trace, finding what's limiting performance, and making sure systems improvements don't change the way the model learns.

What you'll do

• Build and improve supervised fine-tuning, preference optimisation and RL methods
• Work with approaches including PPO, GRPO and SDPO
• Own training loops from rollout generation through to policy updates and checkpointing
• Improve training throughput, GPU utilisation and memory efficiencyProfile and fix bottlenecks across distributed training
• Investigate instability and differences between training and inference
• Use real model failures to improve rewards, training data and overall performance

What you'll need

• Strong programming and quantitative problem-solving skills
• Hands‑on experience with PyTorch and model training
• Understanding of reinforcement learning and/or LLM post‑training
• Experience with distributed training and GPU systems
• Strong experimental judgement
• Ability to work comfortably across ML research and systems engineering

Optional

• vLLM, SGLang, Ray, FSDP or Megatron-LM
• CUDA, Triton, CuTE or GPU performance optimisation

Shortlisted candidates will be contacted within 48 hours.