Machine Learning Engineer, GeForce G-Assist

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $152,000–$241,500 / yr
Employment: Full-time
Posted: 11 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $228k

This role $197k

$137k $293k

below market most similar roles pay here above market

This role pays less than 77% of similar roles. Most pay $202,225–$254,750 — the blue band above. At the midpoint, this role pays about $197k versus about $228k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Machine Learning Engineer, GeForce G-Assist

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The Machine Learning Engineer, GeForce G-Assist joins the team developing an on-device AI assistant that integrates Small Language Models, retrieval systems, and hybrid cloud capabilities. You will evaluate and improve Small Language Models to ensure accuracy, robustness, and conversational reliability while mitigating issues like state drift and prompt leakage. Daily responsibilities include working with SLM and VLM architectures, optimizing local inference using llama.cpp through quantization and memory tuning, and writing performance-critical C/C++ code. You will design retrieval-augmented generation systems to ground responses in user context and support agentic AI workflows for multi-step execution. The role requires proficiency in Python, experience with local inference frameworks, and a deep understanding of conversation dynamics, long-context behavior, and the trade-offs inherent in multimodal model architectures.

What does a Machine Learning Engineer earn in California?

Median $235750 from 200 postings across 34 companies.

See salary data

Skills

C/C++ Python llama.cpp Small Language Models (SLMs) Retrieval-Augmented Generation (RAG) Quantization Local Inference Agentic AI Hybrid Cloud System Software Performance Tuning

What you'll do

Evaluate and improve Small Language Models for accuracy, robustness, and conversational reliability.
Identify and mitigate conversation contamination, state drift, prompt leakage, and retrieval cross-talk.
Optimize local inference using llama.cpp, focusing on quantization, memory usage, and performance tuning.
Write and optimize C/C++ code for performance-critical paths.
Design and integrate retrieval-augmented generation (RAG) systems grounded in system and user context.
Support agentic AI workflows including planning, tool use, and multi-step execution.
Develop hybrid architectures combining local SLMs with cloud-based models.

What we're looking for

5+ years of validated experience in system software or a related field.
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.
Knowledge of SLM and VLM architectures, retrieval technologies, and agentic AI patterns.
Experience contributing to language or multimodal models that power user-facing products (preferred).