AI Infrastructure Engineer (Robotics)

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

Benefits offered by this job

Hybrid work

Job summary

Axiōma Search is seeking a skilled ML engineer to advance post-training methods for complex AI systems. You will improve reinforcement learning, fine-tuning and training loops, while enhancing the infrastructure that drives scalable experiments.

You’ll tackle issues from rollout generation to policy updates, aiming to boost throughput and GPU efficiency in a large-scale environment.

Qualifications

• Strong programming and quantitative problem-solving abilities.
• Hands-on experience with PyTorch and model training.
• Understanding of reinforcement learning or LLM post-training.
• Experience with distributed training and GPU systems.

Responsibilities

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

Skills

Programming

PyTorch

Reinforcement Learning

Distributed Training

GPU Systems

Experimental Judgement

Tools

vLLM

SGLang

Ray

Megatron-LM

Job description

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.

The company 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 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

What you'll need

• Strong programming and quantitative problem-solving skills
• Hands-on experience with PyTorch and model training
• Understanding of reinforcement learning 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.