Machine Learning Engineer, GeForce G-Assist

Details

At NVIDIA, the GeForce G-Assist team is building an on-device AI assistant that combines Small Language Models (SLMs), retrieval systems, and hybrid cloud capabilities to deliver responsive, context-aware assistance inside the GeForce ecosystem. The team works across engineering and product teams to ensure G-Assist performs reliably in real-world scenarios.

Responsibilities

• Evaluate and improve Small Language Models used in GeForce G-Assist, focusing 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.
• 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, including planning, tool use, and multi-step execution.
• Collaborate with product, platform, and systems teams to balance model capability, performance, and user experience.
• Translate user needs and feedback into measurable improvements in model behavior and system reliability.

Requirements

• 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.
• Strong ability to read and write C/C++ code in systems-level or performance-sensitive environments.
• Proficiency in Python.
• Hands-on experience with llama.cpp or similar local inference frameworks.
• Hands-on experience evaluating Small Language Models, including task-based and conversational testing.
• Understanding of conversation dynamics, long-context behavior, and contamination challenges.
• Knowledge of SLM and VLM architectures and their trade-offs.
• Experience with retrieval technologies and language-model integration.
• Familiarity with agentic AI patterns such as tool use and planning.

Preferred Qualifications

• Experience contributing to language or multimodal models that power user-facing products, features, or workflows.
• A track record of collaborating with product, platform, or systems teams.
• Demonstrated ability to translate user needs or feedback into measurable improvements in model behavior or system reliability.

Benefits

• Base salary range of USD 152,000–241,500 for Level 3.
• Base salary range of USD 184,000–287,500 for Level 4.
• Eligibility for equity and benefits.
• Salary is determined based on location, experience, and the pay of employees in similar positions.