AI Performance Optimization Engineer
Tasks
- Build benchmark suites and regression frameworks
- Collaborate with ML and platform engineering teams to standardize best practices
- Document performance tuning playbooks and share findings
- Drive compiler level optimizations with Triton XLA TorchInductor or TVM
- Evaluate new hardware and software offerings and advise on adoption
- Identify and eliminate bottlenecks across data loading model compute communication and memory
- Implement and tune quantization sparsity and pruning
- Improve AI 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 with tensor parallelism pipeline parallelism FSDP and ZeRO style sharding
- Tune attention implementations with FlashAttention and paged attention
Perks/Benefits
Skills/Tech-stack
Benchmarking | C plus plus | CUDA | Compiler optimization | Continuous batching | Deep learning | DeepSpeed | Distributed Training | FSDP | FlashAttention | GPU Architecture | Inference Optimization | KV cache | LLM serving | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Regression testing | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TensorRT | TensorRT-LLM | TorchInductor | Triton | VLLM | XLA | Zero
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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