Responsibilities
- Strong ML Experience: Proficiency in Python and PyTorch to effectively interface with the ML team at a deeply technical level.
- Hardware Awareness: Must understand modern hardware architecture, including cache hierarchies and memory access patterns, and their impact on performance.
- Proficient in Coding: Expertise in Python, PyTorch, and either CUDA, Triton, or C++ is essential for this role.
- Optimization of Low-Level Primitives: Responsible for optimizing core primitives to ensure efficient model execution.
- Self-Guided and Ownership: Ability to independently take a PyTorch model and inference requirements (e.g., maximize GPU throughput or minimize CPU latency) and deliver a fully optimized stack with minimal guidance.
- Research-Driven: Should stay up-to-date with advancements in ML inference, such as new quantization techniques or speculative decoding, while maintaining focus on delivering practical solutions.
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