Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,394 postings in the last year. Stated ranges, not salaries paid.
Machine Learning Engineer, GeForce G-Assist
US, CA, Santa Clara
Live, first seen by us 26 days ago- Pay
Multiple pay amounts — pay source
We are widely considered to be one of the technology world's most desirable employers, and as a result, we have some of the most forward-thinking and hardworking people in the world working for us. If you're passionate, creative, and driven, we'd love to have you join the team. With competitive salaries and a generous benefits package, we are considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us, and due to unprecedented growth, our exclusive engineering teams are rapidly growing. We want to hear from you if you're a creative and autonomous engineer with a real passion for technology. Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4. You will also be eligible for equity and benefits.
Read the full posting- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingEvaluate and improve small language models for accuracy, robustness and conversational reliability
Find and mitigate context contamination such as state drift and prompt leakage
Optimize local inference with llama.cpp, including quantization and memory use
Design and integrate retrieval-augmented generation systems grounded in user context
Support agentic AI workflows with planning, tool use and multi-step execution
From the employer’s posting
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.
Optimize local inference using llama.cpp, including quantization, memory usage, and performance tuning.
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.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 17, 202626 days ago
- Recorded sightings
- 59
- Last seen by us
- Nov 11, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 26
Similar jobs usually come down between day 8 and day 75.
At Nvidia, jobs usually come down by about day 39.
From 5,297 similar postings that came down, and 100 of Nvidia’s. Coming down is not always a hire.
Nvidia is hiring
104 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring is steady
- Takes jobs down at a usual pace
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- C
- Python
- C++
Where the posting names them
- 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.
- 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 environment. - 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, with an understanding of conversation dynamics, long-context behavior, and contamination challenges.
Your next step
Complete your application on nvidia.wd5.myworkdayjobs.com. The employer’s form will show what is required.
Applying through Workday
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Already applied? Track this application
About Nvidia
- People
- About 42,000 in 2026, up 17% from the year before.Annual report (SEC Form 10-K)
- Company
- Listed as NVDA.
- H-1B visas
- 978 approved for new hires in fiscal 2025.US Citizenship and Immigration Services
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
At NVIDIA, we’re building GeForce G-Assist — 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. We work closely across engineering and product teams to ensure G-Assist performs reliably in real-world scenarios.
What you'll be doing:
- 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 environment.
- 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, with an 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, and familiarity with agentic AI patterns such as tool use and planning.
Ways to stand out from the crowd:
- 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 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.
We are widely considered to be one of the technology world's most desirable employers, and as a result, we have some of the most forward-thinking and hardworking people in the world working for us. If you're passionate, creative, and driven, we'd love to have you join the team. With competitive salaries and a generous benefits package, we are considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us, and due to unprecedented growth, our exclusive engineering teams are rapidly growing. We want to hear from you if you're a creative and autonomous engineer with a real passion for technology.
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
We are widely considered to be one of the technology world's most desirable employers, and as a result, we have some of the most forward-thinking and hardworking people in the world working for us. If you're passionate, creative, and driven, we'd love to have you join the team. With competitive salaries and a generous benefits package, we are considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us, and due to unprecedented growth, our exclusive engineering teams are rapidly growing. We want to hear from you if you're a creative and autonomous engineer with a real passion for technology. Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4. You will also be eligible for equity and benefits.
- Location & working pattern
US, CA, Santa Clara
At NVIDIA, we’re building GeForce G-Assist — 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. We work closely across engineering and product teams to ensure G-Assist performs reliably in real-world scenarios. What you'll be doing:
More source context
- 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.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.