Member of Technical Staff (ML Infrastructure & Research)

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

• 1–8 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