Machine Learning Engineer

Work on on-device AI for GeForce G-Assist: help build and improve an on-device AI assistant that uses small language models, retrieval, and hybrid cloud capabilities. This onsite role in Santa Clara, CA focuses on turning model advances into reliable production behavior, with an emphasis on evaluation, multimodal support, and local inference performance.

NVIDIA’s work connects advanced model research to user-facing functionality, so you will spend time on both system-level optimization and AI workflow design. You’ll help ground responses with retrieval, support agentic capabilities like planning and tool use, and ensure conversations stay coherent as models operate locally.

What you’ll do

• Evaluate and improve Small Language Models for GeForce G-Assist, prioritizing accuracy, robustness, and conversational reliability.
• Identify and mitigate issues such as state drift, prompt leakage, and retrieval cross-talk, including conversation and context contamination.
• Support SLM and VLM architectures to enable both text and multimodal interactions.
• Collaborate on hybrid architectures that combine local small language models 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 while working alongside Python.
• Design and integrate retrieval-augmented generation (RAG) systems that ground responses in system and user context.
• Support agentic AI workflows for planning, tool use, and multi-step execution.

What we’re looking for

• 5+ years of validated experience in system software or a related field, with an M.S. or higher in Computer Science, Data Science, Engineering, or a related field (or equivalent experience).
• Strong ability to read and write C/C++ in systems-level or performance-sensitive environments, plus proficiency in Python.
• Hands-on experience with llama.cpp or similar local inference frameworks.
• Hands-on experience evaluating Small Language Models using task-based and conversational testing, including 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, and familiarity with agentic AI patterns such as tool use and planning.

Technologies you’ll work with

• C/C++, Python, llama.cpp
• Small Language Models (SLMs), VLM architectures
• Retrieval-augmented generation (RAG)
• Agentic AI workflows

How you can stand out

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

Compensation and details

Base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is USD 152,000 - 241,500 for Level 3, and USD 184,000 - 287,500 for Level 4.

• You may be eligible for equity and benefits.
• Applications accepted at least until October 20, 2026.
• This posting is for an existing vacancy.
• NVIDIA uses AI tools in its recruiting processes.