About the job
Join the new Agentic Engineering team, within the Deep Learning Framework Group, at NVIDIA. We build the agentic workflows that automate code generation, testing, and tuning across NVIDIA's frameworks, compilers, and developer tooling. The team is a force multiplier for the engineers behind that stack. This greenfield opportunity offers foundational technical influence within a high-autonomy team inside Deep Learning Frameworks. We partner directly with early-adopter teams to translate complex requirements into durable, scalable infrastructure that other teams can adopt. The work sits at a genuinely rare intersection: modern AI applied to the craft of engineering itself, inside a company whose hardware powers the AI revolution.
Responsibilities
- Develop a deep, shared understanding with NVIDIA's early-adopter engineering teams, identifying the friction points where agentic workflows would have the highest impact.
- Iterate with early-adopter teams on proof points to validate or revise plans together.
- Use technical judgment to distinguish durable architectural opportunities from 'tech du jour' hype.
- Agent-ify compiler infrastructure to enable autonomous agents to make high-dimensional optimizations, with closed-loop validation on real hardware.
- Integrate systems into git-native workflows and CI pipelines so agents can build, test, and iterate against real GPUs.
- Contribute to cross-org collaborative group sharing reusable agentic methodology, helping the broader organization adopt what works.
Qualifications
Minimum
- MS in Computer Science, Engineering, or equivalent experience
- 6+ years of experience.
- Strong Python development skills
- Working knowledge of GPUs or other highly data-parallel systems
- Demonstrated projects or work experience using and supporting AI systems
- Track record of shipping complex projects with minimal direction, including raising challenges or syncing at the right moments
- Experience building tools or systems shaped by direct partnership with internal customer or user teams
- Examples of leading technical work through changing requirements and revising direction when evidence demands it
Preferred
- Passion for following the evolution of ML hardware and staying up to date on emerging kernel programming techniques
- Experience building evaluation or testing harnesses, especially for ML systems or multi-agent workflows
- Track record of building internal tools or frameworks that force-multiply engineering teams
- Demonstrated ability to thrive in ambiguous, self-directed environments while remaining humble: communicating with clarity, actively listening, and finding ground truth
- An allergic reaction to 'solutions in search of problems'