ML Infrastructure Engineer
Tasks
- Benchmark GPU platform performance
- Conduct acceptance testing for new GPU clusters
- Debug and optimize training and inference workloads
- Develop internal tools and dashboards for performance metrics
- Optimize distributed training and inference acceleration
- Profile GPU performance at system and kernel level
- Run experiments across GPU configurations and interconnect strategies
Perks/Benefits
- Career development opportunities
- Flexible remote work
- International work environment
- Technical ownership and autonomy
- Work-life balance
Skills/Tech-stack
AWS | Azure | Batching | CUDA | Deep learning | Distributed Training | Docker | GPU Performance | GPU Performance Optimization | GPU drivers | Google Cloud | JAX | Kernel optimization | Kubernetes | LLM Inference | Machine Learning | Megatron-LM | NCCL | NVProf | Nsight | Parallelism | Perf | Performance optimization | PyTorch | Python | SGLang | TensorRT | TensorRT-LLM | VLLM
Education
N/A
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