AI Performance Optimization Engineer
Tasks
- Build and maintain benchmark suites and regression frameworks
- Collaborate to embed best practices in standard pipelines
- Document performance tuning playbooks and share findings
- Drive compiler level optimizations
- Evaluate new hardware and software offerings
- Identify and eliminate throughput latency and cost bottlenecks
- Implement and tune quantization sparsity and pruning
- Improve cost efficiency through architecture hardware selection and scheduling
- Optimize KV cache and implement continuous batching and speculative decoding
- Optimize data pipelines sharding strategies and storage access
- Optimize distributed training with tensor parallelism and pipeline parallelism
- Profile and optimize AI training and inference pipelines
- Stay current with AI research and translate into production improvements
- Tune attention implementations
Perks/Benefits
- Career growth potential
- Full-time benefits
- H1B transfer support for qualified candidates
- Long-term engagement
- Remote work
Skills/Tech-stack
Benchmarking | C++ | Continuous batching | Cutlass | DeepSpeed | FSDP | FlashAttention | GPU Architecture | KV cache | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Regression testing | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TensorRT-LLM | TorchInductor | Triton | VLLM | XLA | Zero
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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