Senior AI Engineer, AI Safety

NVIDIA

. Compose, build, and deploy foundational agentic automation loops for safety evaluations, red-teaming, model testing, model post-training, and ablations .

Posted 11/7/2026full-timeRemote • California • United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying automated systems for AI safety, including proficiency in LLMs and machine learning frameworks. Capable of managing complex projects from conception to scalable production, with a strong focus on trust and safety in AI applications.

Highest-signal resume keywords

Machine Learning EngineeringLLM DevelopmentPython ProgrammingTrust & Safety Domain ExperienceAutomated Workflow Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningAutomated WorkflowsModel DeploymentHarm DetectionContent Safety FilteringRust ProgrammingPyTorch FrameworkResearch FrameworksData SynthesisAdversarial Machine Learning

Soft Skills

Problem SolvingProject OwnershipCollaboration

Tools & Technologies

AI Safety Benchmarking ToolsAgent Frameworks

Industry Keywords

CybersecurityThreat IntelligenceContent Moderation

Tech Stack

Tools & technologies

Cyber SecurityPythonPyTorchRust

About the role

Key responsibilities & impact

Compose, build, and deploy foundational agentic automation loops for safety evaluations, red-teaming, model testing, model post-training, and ablations
Train and implement specialized systems for harmful-content identification, content quality coordination, and run-time guard-railing
Build and guide automated research frameworks using LLMs and autonomous agents to discover vulnerabilities
Generate or augment safety datasets for benchmarking or training and propose novel alignment techniques
Engineer proof-of-concept safety interventions and research ideas into robust, scalable production solutions
Partner with researchers, platform engineers, and data scientists to connect AI safety research with practical automated systems

Requirements

What you’ll need

Bachelor’s degree or Master’s degree with equivalent experience in Computer Science, Machine Learning, Software Engineering, or a related field
8+ years of experience in ML engineering or software engineering
Proven experience building complex, automated workflows using LLMs, including multi-turn tool-calling, orchestration, and autonomous agent loops
Deep proficiency in one or more languages such as Rust or Python
Experience with machine learning frameworks like PyTorch
Ability to tackle open-ended, ambiguous problems and experiment, fail, iterate, and pursue solutions
Ability to own a project from initial white-boarding of a research idea to final deployment of a scalable production system
Prior domain experience in Trust & Safety, cybersecurity, threat intelligence, adversarial machine learning, or content moderation
Hands-on experience developing, fine-tuning, or deploying models for harm detection, content safety filtering, or run-time AI guardrails
Contributions to projects using LLMs to automate coding, data synthesis, or scientific research tasks
Active contributions to publicly available agent frameworks or AI safety benchmarking tools

Benefits

Comp & perks

Equity
Comprehensive benefits package
Competitive salary