Research Engineer/Scientist - Machine Learning RL & Optimisation (Contractor)
Tasks
- Architect distributed training experiments
- Build training infrastructure with parallelization frameworks
- Design benchmark and deploy optimized tensor operators
- Design dense reward functions and synthetic data pipelines
- Design reinforcement learning finetuning workflows
- Develop large-scale machine learning systems
- Implement profiling and debugging pipelines
- Integrate inference engines into RL generation loops
- Optimize training throughput and memory utilization
Perks/Benefits
- Additional time off for learning and development
- Annual leave
- Cycle to work scheme
- Employee assistance program
- Group personal pension
- Life insurance
- Medical expense reimbursement
- Pension
- Private medical insurance
Skills/Tech-stack
Bayesian optimization | Data Generation | Debugging | DeepSpeed | Distributed Systems | Distributed Training | FSDP | FlashAttention | GEMM | GPU Profiling | Human Feedback | Kernel performance | Language Models | Large Language Models | Learning from Human Feedback | Machine Learning | Memory Optimization | Policy Optimization | Proximal Policy Optimization | PyTorch | Reinforcement Learning | Reinforcement Learning from Human Feedback | Synthetic Data Generation | Synthetic data | TileLang | Triton | VLLM
Education
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