AI Performance Optimization Engineer
Tasks
- Build benchmark suites and regression frameworks
- Collaborate with ML and platform engineering teams on best practices
- Document performance tuning playbooks
- Drive compiler level optimizations using Triton XLA Torch Inductor and TVM
- Evaluate new hardware and software offerings
- Identify bottlenecks across data loading model compute communication and memory
- Implement and tune quantization sparsity and pruning
- Improve cost efficiency through model architecture hardware selection and scheduling
- Optimize AI training and inference pipelines for throughput latency and cost
- Optimize KV cache and continuous batching and speculative decoding for LLM serving
- Optimize data pipelines sharding and storage access patterns
- Optimize distributed training using tensor parallelism pipeline parallelism FSDP and ZeRO sharding
- Research AI performance advances and translate into production improvements
- Tune attention implementations using Flash Attention paged attention and related techniques
Perks/Benefits
Skills/Tech-stack
Benchmarking | C++ | Compiler optimization | Continuous batching | Deep learning | Distributed Training | FSDP | Flash Attention | GPU Architecture | KV cache | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Quantization aware training | Regression testing | Sparsity | Speculative decoding | TVM | Tensor Parallelism | Torch Inductor | Triton | XLA | Zero
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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