AI Performance Optimization Engineer
Tasks
- Build benchmark suites and regression frameworks
- Collaborate with ML and platform engineering teams to embed best practices
- Document performance tuning playbooks
- Drive compiler level optimization using Triton XLA Torch Inductor or TVM
- Evaluate new hardware and software offerings and advise on adoption
- Identify and eliminate bottlenecks in 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 continuous batching and speculative decoding for LLM serving
- Optimize data pipelines sharding strategies and storage access patterns
- Optimize distributed training using tensor parallelism pipeline parallelism FSDP and ZeRO style sharding
- Research AI systems improvements and translate to production
- Tune attention implementations using Flash Attention and paged attention
Perks/Benefits
Skills/Tech-stack
Benchmarking | C++ | Compiler optimization | Continuous batching | Cutlass | DeepSpeed | Distributed Training | FSDP | Flash Attention | GPU Architecture | Hardware selection | KV cache | Memory Management | Model Parallelism | Paged Attention | Parallelism | Performance Engineering | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Regression testing | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TensorRT-LLM | Torch Inductor | Triton | VLLM | XLA | Zero
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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