GPU Performance Architect
2 days ago
Liquid AI is seeking a highly skilled AI Inference Expert to join our team. As a key member of our engineering team, you will be responsible for optimizing inference stacks tailored to various hardware platforms, including GPUs, CPUs, and NPUs. If you have a passion for delivering exceptional performance and low latency, we want to hear from you.
Job Requirements
The ideal candidate is a highly skilled engineer with extensive experience in CUDA, C++, and Triton, as well as a deep understanding of GPU, CPU, and NPU architectures. Proficiency in building and enhancing inference stacks using frameworks like ggml, vllm, and DeepSpeed is essential. Additionally, experience with mobile development and expertise in cache-aware algorithms will be highly valued.
- 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.
We offer a competitive salary of $190,000 - $230,000 per annum, depending on experience, plus benefits including health insurance, retirement plan, and generous paid time off. Our team is passionate about innovation and collaboration, and we strive to create a workplace that is inclusive and respectful.
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