AI Optimization Engineer
Tasks
- Build benchmark suites and regression frameworks
- Document performance tuning playbooks
- Drive compiler level optimizations using Triton XLA TorchInductor or TVM
- Embed best practices in standard pipelines
- Evaluate new hardware and software offerings
- Identify and eliminate bottlenecks
- Implement quantization sparsity and pruning
- Improve cost efficiency through scheduling hardware selection and architecture
- Minimize latency and cost
- Optimize KV cache continuous batching and speculative decoding
- Optimize data pipelines and storage access patterns
- Optimize distributed training using tensor parallelism pipeline parallelism and sharding
- Profile and optimize AI inference pipelines
- Profile and optimize AI training pipelines
- Translate AI research into production improvements
- Tune attention implementations using FlashAttention and paged attention
Perks/Benefits
Skills/Tech-stack
Benchmarking | C++ | Cache optimization | Compiler optimization | Continuous batching | Deep learning | Distributed Training | FSDP | FinOps | FlashAttention | GPU Architecture | KV cache | KV cache optimization | Memory Management | Model Parallelism | Paged Attention | Pipeline parallelism | Profiling tools | Pruning | Python | Quantization | Regression testing | Sharding | Sparsity | Speculative decoding | TVM | Tensor Parallelism | TorchInductor | Triton | XLA | Zero
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Optimization Engineer | Engineer | Optimization Engineer
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