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