ML Systems Engineer — RL & Post-Training

Axiōma Search; London; On-site; NOK 1,154,000 - 1,538,000; Full time

Job summary

Axiōma Search is seeking a researcher to advance models after initial training, focusing on reinforcement learning and post-training methods. You will bridge ML research and the systems that run experiments at scale, from algorithmic improvements to GPU profiling and infrastructure tuning.

You'll collaborate across learning algorithms and the underlying systems, pushing throughput, reliability, and efficiency while keeping the learning signal intact.

Qualifications

• 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 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

Programming skills

PyTorch

Reinforcement learning

Distributed training

Experimentation

ML systems

Tools

Ray

Megatron-LM

vLLM

Job description

Axiōma Search is seeking a researcher to advance models after initial training, focusing on reinforcement learning and post-training methods. You will bridge ML research and the systems that run experiments at scale, from algorithmic improvements to GPU profiling and infrastructure tuning.

You'll collaborate across learning algorithms and the underlying systems, pushing throughput, reliability, and efficiency while keeping the learning signal intact.