Senior AI Engineer, AI Safety

NVIDIA•Santa Clara, CA

About The Position

NVIDIA is hiring a Senior AI Engineer for its AI Safety Engineering team to help scale methods for securing and aligning advanced AI systems. The role will architect and develop foundational safety infrastructure, with an emphasis on autonomous automation cycles, and partner with Research and Model Development teams to deliver models and workflows for harm detection, content moderation, dynamic guardrails, post-training, and automated research. The position is suited to an engineer who is comfortable with ambiguity and can turn early-stage safety concepts into robust, scalable implementations. Work spans building agentic automation, developing specialized safety models, creating automated research frameworks, integrating proof-of-concept interventions into production pipelines, and collaborating across research and engineering disciplines.

Requirements

Bachelor’s or master’s degree, or equivalent experience, in Computer Science, Machine Learning, Software Engineering, or a related field.
At least 8 years of experience in machine learning 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 programming languages, such as Rust or Python.
Experience with machine learning frameworks such as PyTorch.
Ability to write scalable, maintainable, and highly optimized code.
Ability to tackle open-ended, ambiguous problems and experiment, iterate, and persist through difficult technical challenges.
Ability to own projects from research conception through production deployment.

Nice To Haves

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 runtime AI guardrails.
Contributions to projects using LLMs to automate coding, data synthesis, or scientific research.
Contributions to public agent frameworks such as LangChain, AutoGPT, or Semantic Kernel, or to AI safety benchmarking tools.

Responsibilities

Architect, build, and deploy agentic automation loops for safety evaluations, red-teaming, model testing, model post-training, and ablations.
Develop and implement specialized systems for harmful-content identification, content quality coordination, and runtime guardrails.
Build and guide automated research frameworks that use LLMs and autonomous agents to discover vulnerabilities, generate or augment safety datasets, and propose alignment techniques.
Engineer proof-of-concept safety interventions and research ideas into robust, scalable solutions integrated with production pipelines.
Partner with researchers, platform engineers, and data scientists to turn AI safety research into practical automated systems.
Own projects from initial research-idea whiteboarding through deployment of scalable production systems.