Machine Learning Engineer, AI Safety

Nvidia

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CA
Salary: $124,000–$195,500 / yr
Employment: Full-time
Posted: 5 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 $213k

This role $160k

$107k $282k

below market most similar roles pay here above market

This role pays less than 87% of similar roles. Most pay $179,231–$246,150 — the blue band above. At the midpoint, this role pays about $160k versus about $213k 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 1463 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1096 roles with salary data.

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At a glance

TL;DR · Machine Learning Engineer, AI Safety

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The Machine Learning Engineer, AI Safety joins a team dedicated to ensuring content safety, robustness, and explainability for generative models. In this role, you will develop datasets and models to train and evaluate end-to-end systems for Product Security, Content Safety, and ML Fairness. You will research and implement cutting-edge techniques for bias detection and mitigation in large language models, including RAG systems. Key responsibilities involve defining metrics for responsible behavior, following MLOps practices for automation and monitoring, and developing safety tools for other engineering teams. The position requires proficiency in Python and frameworks like Keras or PyTorch. You will work with large multi-modal datasets to solve technical problems regarding hallucinations, generative misinformation, and adversarial robustness across various research and production engineering environments.

What does a Machine Learning Engineer earn in California?

Median $231150 from 187 postings across 27 companies.

See salary data

Skills

Python PyTorch Keras LLMs MLOps Deep Learning Multi-modal Models Fine-tuning Bias Detection Adversarial Robustness AI Security

What you'll do

Develop datasets and models for training and evaluating content safety, robustness, and ML fairness systems.
Research and implement cutting-edge techniques for bias detection and mitigation in LLMs and RAG systems.
Define and track key metrics for responsible LLM behavior and usage.
Apply MLOps best practices to ensure automation, monitoring, scale, and safety across production systems.
Contribute to the MLOps platform and develop safety tools to improve ML team effectiveness.
Assess, quantify, and improve the safety and inclusivity of multi-modal LLM models in a scalable fashion.

What we're looking for

Master’s or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
Minimum of 2+ years of work experience in developing and deploying machine learning models in production.
Strong understanding of machine learning principles and algorithms.
Hands-on programming experience in Python and in-depth knowledge of machine learning frameworks like Keras or PyTorch.
Background in Content Safety, ML Fairness, Robustness, AI Model Security, or related areas for 1+ years.
Experience working with large multi-modal datasets and multi-modal models.
Skilled with alignment/fine-tuning of LLMs, including regular LLMs as well as VLMs or any-to-text (preferred).
Proven experience with multimodal and/or multilingual content safety, legal, and regulatory compliance (preferred).

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