ML Infrastructure Engineer
Tasks
- Benchmark GPU platform performance
- Build internal performance dashboards
- Debug and optimize inference workloads
- Debug and optimize training workloads
- Develop benchmarking methodologies
- Optimize distributed training and inference
- Profile GPU performance at system and kernel level
- Run experiments across GPU configurations
- Validate GPU cluster performance and stability
Perks/Benefits
- Career development
- Continuous learning
- Flexible remote work
- International work environment
- Technical ownership and autonomy
- Work-life balance
Skills/Tech-stack
Batching | CUDA | Deep learning | Distributed Training | Distributed inference | Docker | GPU Performance | GPU drivers | GPU performance profiling | JAX | Kernel optimization | Kubernetes | Machine Learning | Megatron-LM | NCCL | NVProf | Nsight | Parallelism | Perf | Performance Profiling | Performance Tuning | PyTorch | Python | TensorRT | TensorRT-LLM
Education
N/A
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