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 performance reporting frameworks
- Develop internal tools and dashboards
- Improve distributed training and inference performance
- Profile GPU performance at system and kernel level
- Run experiments across GPU configurations and interconnect strategies
- Support hardware aware performance tuning
Perks/Benefits
- Collaborative engineering culture with technical ownership
- Continuous learning and career development
- Flexible remote work environment
- International work environment
- Work-life balance
Skills/Tech-stack
AWS | Azure Machine Learning | Batching | CUDA | CUDA profiling | Cloud platform | Data parallelism | Distributed Training | Docker | GPU drivers | Google Cloud | Google Cloud Platform | JAX | Kubernetes | LLM Inference | Machine Learning | Megatron-LM | Model Parallelism | NCCL | NVProf | Nsight | Perf | PyTorch | Python | SGLang | TensorRT | TensorRT-LLM | VLLM
Education
N/A
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