Senior Software Engineer, Deep Learning Libraries

About the position

NVIDIA's Deep Learning Safety team is looking for a Senior Software Engineer to build intelligent, autonomous software for the next generation of accelerated computing. At NVIDIA, we are leading the future of AI and accelerated systems. Our Deep Learning Safety team is looking for a Senior Software Engineer to design and implement AI-driven agents that autonomously interact with APIs, tools, and environments to accomplish complex goals. This role bridges the worlds of large language models (LLMs), systems programming, and intelligent automation. If you're passionate about building software that thinks and acts, this is your opportunity to define the future.

Responsibilities

• Design and develop intelligent software agents capable of planning, reasoning, and acting across tools, APIs, and compute environments.
• Integrate agentic AI systems with NVIDIA platforms, including GPU computing frameworks and runtime engines.
• Work with LLMs and agentic frameworks to build robust, reactive, and proactive agents.
• Build end-to-end systems that use agents for tasks such as kernel optimization, code generation, performance analysis.

Requirements

• BS/MS in Computer Science, AI, or equivalent experience.
• 5+ years of software engineering experience.
• Strong programming experience in Python and C++ (or Go/Rust), including experience with async runtimes and API orchestration.
• Experience building applications or systems that incorporate LLMs or foundation models in an interactive or autonomous fashion.
• Familiarity with tools and frameworks for building AI agents (e.g., LangChain, CrewAI).
• Proven systems thinking and ability to design for safety, observability, and robustness in autonomous execution.
• Strong communication skills, excellent problem-solving skills, and the ability to learn and work effectively in a fast-paced, collaborative environment.

Nice-to-haves

• Demonstrated work on agent-based systems, autonomous workflows, or intelligent software automation.
• Experience integrating AI with GPU computing workflows (e.g., CUDA, PyTorch, Triton, or compiler toolchains).
• Knowledge of planning algorithms or program synthesis using LLMs.
• Contributions to open-source AI agent frameworks or agentic copilots.

Benefits

• Equity and benefits.