ML Performance Engineer
Tasks
- Build and maintain benchmark suites and regression frameworks
- Collaborate with ML and platform teams to embed performance best practices
- Document performance tuning playbooks and share findings
- Drive compiler level optimizations with Triton XLA TorchInductor and TVM
- Evaluate new hardware and software and advise adoption
- Identify and eliminate bottlenecks in data loading model compute communication and memory
- Implement and tune quantization sparsity and pruning strategies
- Optimize KV cache continuous batching and speculative decoding
- Optimize data pipelines sharding and storage access patterns
- Optimize distributed training using tensor parallelism pipeline parallelism FSDP and ZeRO sharding
- Optimize training and inference pipelines for throughput latency and cost
- Tune attention implementations using FlashAttention and paged attention
Perks/Benefits
- N/A
Skills/Tech-stack
Attention Mechanisms | Benchmarking | C++ | Compiler optimization | Continuous batching | Deep learning | Distributed Training | FSDP | FlashAttention | GPU Architecture | Inference Optimization | KV cache | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TorchInductor | Triton | XLA | Zero
Education
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