ML Infrastructure Engineer
Tasks
- Analyze debug and optimize inference workloads
- Analyze debug and optimize training workloads
- Benchmark GPU platform performance
- Conduct acceptance testing for GPU clusters
- Create reporting frameworks for benchmarking
- Develop internal tools and dashboards for performance metrics
- Implement distributed training and inference performance tuning
- Profile GPU performance at system and kernel level
- Run experiments across GPU configurations and interconnects
Perks/Benefits
- Career development
- Collaborative innovation culture
- Continuous learning opportunities
- Flexible remote work
- International work environment
- Technical ownership and autonomy
Skills/Tech-stack
AWS | Azure ML | Batching | CUDA | Cloud platform | Deep learning | Docker | GPU Performance | GPU Performance Optimization | GPU drivers | Google Cloud | Google Cloud Platform | JAX | Kernel optimization | Kubernetes | LLM Inference | Machine Learning | Megatron-LM | NCCL | Nsight | Parallelism | Perf | Performance optimization | Profiling | PyTorch | SGLang | TensorRT | TensorRT-LLM | VLLM
Education
N/A
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