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.
What you'll be doing:
Our initial customers are NVIDIA's early-adopter engineering teams. You will develop a deep, shared understanding with them, identifying the friction points where agentic workflows would have the highest impact. Requirements will evolve as these teams integrate agents into production, so you will iterate with them on proof points to validate or revise your plans together. As an applied ML expert, you will use technical judgment to distinguish durable architectural opportunities from "tech du jour" hype.
The work spans several areas. You might agent-ify compiler infrastructure to enable autonomous agents to make high-dimensional optimizations, with closed-loop validation on real hardware. Multi-agent orchestration is core, anything from LLM-native tooling to custom work with frameworks like LangChain/LangGraph, driving autonomous loops that apply changes, measure results, ratchet forward and repeat. We integrate these systems into git-native workflows and CI pipelines so agents can build, test, and iterate against real GPUs. Familiarity with NVIDIA's latest GPUs comes with the territory, since the work targets the teams that support them. We contribute to cross-org collaborative group sharing reusable agentic methodology, helping the broader organization adopt what works.
What We Need To See:
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
Experience in one or more of the following areas:
Multi-agent orchestration frameworks (e.g., LangChain, LangGraph) or LLM-based workflow automation
Compiler infrastructure, intermediate representations, or program transformation
Autonomous search or optimization over high-dimensional parameter spaces
Hardware-aware performance optimization for deep learning workloads
Code generation systems or domain-specific languages (DSLs)
Ways to stand out from the crowd:
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"
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- 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
- 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
- Multi-agent orchestration frameworks (e.g., LangChain, LangGraph) or LLM-based workflow automation
- Compiler infrastructure, intermediate representations, or program transformation
- Autonomous search or optimization over high-dimensional parameter spaces
- Hardware-aware performance optimization for deep learning workloads
- Code generation systems or domain-specific languages (DSLs)
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
