AI Performance Optimization Engineer
United States - Remote
R
USD 136K-258K (estimate) Mid-level Full Time
Tasks
- Build benchmark suites and regression frameworks
- Collaborate with ML and platform engineering teams
- Document performance tuning playbooks
- Drive compiler level optimizations
- Evaluate hardware and software for adoption
- Identify and eliminate throughput latency and memory bottlenecks
- Implement and tune quantization sparsity and pruning
- Improve AI cost efficiency through architecture and scheduling
- Optimize KV cache continuous batching and speculative decoding
- Optimize data pipelines sharding and storage access patterns
- Optimize distributed training with parallelism and sharding
- Profile and optimize AI training and inference pipelines
- Translate AI research advances to production
- Tune attention implementations for performance
Perks/Benefits
- N/A
Skills/Tech-stack
Access Optimization | Attention Optimization | Benchmarking | C++ | Compiler optimization | Continuous batching | Data loading | Data loading optimization | Deep learning | Distributed Training | FSDP | FlashAttention | GPU Architecture | KV cache | Loading Optimization | Memory Management | Model Compression | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling | Pruning | Python | Quantization | Quantization aware training | Regression testing | Sparsity | Speculative decoding | Storage Access | Storage Access Optimization | TVM | Tensor Parallelism | TorchInductor | Triton | XLA | Zero
Education
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