Machine Learning Engineer, GeForce G-Assist

Responsibilities

• Together, we focus on how models behave in production, not just on benchmarks. Evaluate and improve Small Language Models used in GeForce G-Assist, with an emphasis on accuracy, robustness, and conversational reliability.
• Identify and mitigate conversation and context contamination, including state drift, prompt leakage, and retrieval cross-talk.
• Work with SLM and VLM architectures to support text and multimodal interactions.
• Collaborate on hybrid architectures that combine local SLMs with cloud-based models. We value engineers who enjoy thinking across the full system—from model behavior to runtime performance.
• Optimize local inference using llama.cpp, including quantization, memory usage, and performance tuning. Read, write, and optimize C/C++ code in performance-critical paths.
• Design and integrate retrieval-augmented generation (RAG) systems that ground responses in system and user context.
• Support agentic AI workflows, enabling planning, tool use, and multi-step execution.
• What we need to see
• 5+ years of validated experience in system software or a related field, with an M.S. or higher degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience). We’re looking for teammates who enjoy solving real problems, learning as they go, and collaborating in a tight-knit…
• Strong ability to read and write C/C++ code in systems-level or performance-sensitive environments, along with proficiency in Python. Hands-on experience with llama.cpp or similar local inference frameworks.

Languages