AI Infrastructure Engineer (Robotics)

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 methodsWork with approaches including PPO, GRPO and SDPOOwn training loops from rollout generation through to policy updates and checkpointingImprove training throughput, GPU utilisation and memory efficiencyProfile and fix bottlenecks across distributed trainingInvestigate instability and differences between training and inferenceUse real model failures to improve rewards, training data and overall performance What you'll need Strong programming and quantitative problem-solving skillsHands-on experience with PyTorch and model trainingUnderstanding of reinforcement learning or LLM post-trainingExperience with distributed training and GPU systemsStrong experimental judgementAbility to work comfortably across ML research and systems engineering Optional vLLM, SGLang, Ray, FSDP or Megatron-LMCUDA, Triton, CuTE or GPU performance optimisation Shortlisted candidates will be contacted within 48 hours. #J-18808-Ljbffr