Member of Technical Staff

Responsibilities

• Measure and improve how fast and efficiently specialized language models run in real production use including response time throughput GPU usage memory caching batching and model compression (quantization).
• Build testing setups scoring tools and reinforcement learning environments from real customer AI-agent activity so that day-to-day usage feeds a steady cycle of model improvement.
• Work directly with the founders and business partners to put improved models into important customer workflows that have strict reliability targets.
• Turn successful research experiments into reusable building blocks that teammates customer agents and production systems can use.
• Build and maintain the tools that manage GPU allocation across model training and live serving.

Requirements

• 18 years of hands-on experience in ML infrastructure inference optimization or applied LLM research engineering at a fast-growing AI startup or a leading AI lab.
• Strong skills in Python and PyTorch plus at least some of C/CUDA Triton or JAX.
• Proven experience speeding up LLM serving tools such as vLLM SGLang or TensorRT-LLM.
• Self-driven and comfortable taking high ownership.
• Able to work in person 5 days a week in the Bay Area.

Skills Required

• LLM inference optimization & serving (vLLM SGLang TensorRT-LLM)
• Python & PyTorch
• C / CUDA / Triton / JAX
• Reinforcement learning post-training (RLHF/RLAIF)
• Model evaluation & grader design
• Synthetic data generation
• GPU resource management
• ML infrastructure engineering