ML Infrastructure Engineer
Tasks
- Benchmark GPU platform performance
- Build internal benchmarking tools
- Conduct experiments across GPU configurations
- Debug GPU training and inference workloads
- Develop performance dashboards and reporting
- Evaluate AI workloads
- Improve distributed training and inference performance
- Optimize training and inference efficiency
- Profile GPU performance at system and kernel level
- Run acceptance testing for GPU clusters
- Tune hardware aware parallelism strategies
- Validate performance stability compatibility readiness
Perks/Benefits
- Autonomy
- Career development
- Continuous learning
- Flexible remote work
- International collaboration
- Technical ownership
Skills/Tech-stack
Benchmarking | CUDA | Data parallelism | Deep learning | Distributed Training | Docker | GPU drivers | JAX | Kubernetes | Machine Learning | Megatron-LM | Model Parallelism | NCCL | NVProf | Nsight | Parallelism | Perf | PyTorch | Python | SGLang | TensorRT | TensorRT-LLM | VLLM
Education
N/A
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