ML Infrastructure Engineer
Tasks
- Analyze debug and optimize inference workloads
- Analyze debug and optimize training workloads
- Benchmark GPU platform performance for machine learning workloads
- Conduct acceptance testing for GPU clusters
- Develop internal tools dashboards and reporting frameworks
- Implement infrastructure best practices for benchmarking and optimization
- Optimize distributed training and inference acceleration
- Perform experiments across GPU configurations and interconnect strategies
- Profile GPU performance at system and kernel levels
Perks/Benefits
- Career development
- Continuous learning
- Flexible remote work
- International work environment
- Technical ownership
- Worklife balance
Skills/Tech-stack
CUDA | Deep learning | Distributed Training | Docker | GPU Performance | GPU Performance Optimization | GPU benchmarking | Inference acceleration | JAX | Kubernetes | Machine Learning | Megatron-LM | NCCL | NVIDIA Nsight | Parallelism | Performance optimization | PyTorch | Python | SGLang | TensorRT | TensorRT-LLM | VLLM
Education
N/A
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