AI Performance Optimization Engineer
Tasks
- Build benchmark suites and regression frameworks
- Collaborate to embed best practices in production pipelines
- Document performance tuning playbooks and share findings
- Drive compiler level optimizations for end to end performance
- Evaluate new hardware and software and recommend adoption
- Identify and eliminate performance bottlenecks
- Implement and tune quantization sparsity and pruning
- Improve cost efficiency through model hardware and scheduling
- Optimize AI training and inference pipelines for throughput latency and cost
- Optimize KV cache continuous batching and speculative decoding
- Optimize data pipelines and sharding for training throughput
- Optimize distributed training with tensor and pipeline parallelism
- Translate AI research advances into production improvements
- Tune attention implementations for faster inference
Perks/Benefits
- N/A
Skills/Tech-stack
Access Optimization | Attention Mechanisms | Benchmarking | C plus plus | CPU | Cache optimization | Compiler optimization | Continuous batching | Data Sharding | Data loading | Data loading optimization | DeepSpeed | Distributed Training | FSDP | FlashAttention | GPU Architecture | GPU Profiling | KV cache | KV cache optimization | Loading Optimization | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling tools | Pruning | Python | Quantization | Regression testing | Sparsity | Speculative decoding | Storage Access | Storage Access Optimization | TVM | Tensor Parallelism | TensorRT | TorchInductor | Triton | XLA | Zero
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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