Machine Learning Engineer, GeForce G-Assist - CA, Santa Clara, United States
$152,000 - $287,500 USD yearly
Originally posted 16 October 2026 by the employer.
Work on GeForce G-Assist, an on-device AI assistant combining Small Language Models, retrieval systems, and hybrid cloud capabilities.
About the role
As a Machine Learning Engineer focusing on GeForce G-Assist, you will evaluate and improve Small Language Models (SLMs) used in an on-device AI assistant, ensuring accuracy, robustness, and conversational reliability in production scenarios. This role involves optimizing local inference using llama.cpp and designing retrieval-augmented generation (RAG) systems.
What you'll do
- Evaluate and improve Small Language Models (SLMs) for GeForce G-Assist, emphasizing 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, collaborating 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, supporting agentic AI workflows.
What you'll need
- 5+ years of 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, along with 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.
Skills: Small Language Models, RAG systems, local inference, C++, Python
This role has been open 1 day — well below the 51-day median for Infrastructure/Platform Software roles.
Infrastructure/Platform Software · Infrastructure Platform
|
Open roles in category
962
|
Median days open
51 d
|
Median salary
$236k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | NVIDIA | All employers we track in this specialty (962 roles · 86 employers) |
|---|---|---|
| Open roles in this specialty | 278 | 962 |
| Open roles in the wider Software, Firmware & Systems family | 971 | 6118 · 145 employers |
| Median days open | 46 d | 51 d (−5 d vs this employer) |
| Median salary (USD postings) | — | $236k |
Skills observed across this category: Small Language Models, RAG systems, local inference, C++, Python
Who's hiring in this category
- NVIDIA (this employer) · 274 open roles · median 47 d
- Qualcomm · 62 open roles · median 53 d
- AMD · 54 open roles · median 19 d
- Graphcore · 44 open roles · median 93 d
- Intel Corporation · 43 open roles · median 12 d
- Cerebras · 36 open roles · median 83 d
How we counted: 962 open Infrastructure/Platform Software (Infrastructure Platform) roles from 86 employers tracked in the SemiconductorJobs index, counted 18 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.