Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
Agentic Systems
Deep Learning @ 6
GPU
Git
LLM @ 6
LangChain @ 6
Machine Learning
Performance Optimization @ 6
Python @ 7
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Join the new Agentic Engineering team within NVIDIA's Deep Learning Framework Group. The team builds agentic workflows that automate code generation, testing, and tuning across NVIDIA's frameworks, compilers, and developer tooling. This greenfield opportunity offers foundational technical influence within a high-autonomy team in Deep Learning Frameworks. The team partners directly with early-adopter teams to translate complex requirements into durable, scalable infrastructure.
Responsibilities
- Develop a shared understanding with NVIDIA's early-adopter engineering teams and identify friction points where agentic workflows can have the highest impact.
- Iterate with engineering teams on proof points as they integrate agents into production, validating or revising plans based on evidence.
- Apply technical judgment to distinguish durable architectural opportunities from temporary technology trends.
- Build agentic workflows for compiler infrastructure, enabling autonomous agents to perform high-dimensional optimizations with closed-loop validation on real hardware.
- Develop multi-agent orchestration systems using LLM-native tooling and frameworks such as LangChain and LangGraph.
- Build autonomous loops that apply changes, measure results, and iteratively improve outcomes.
- Integrate agentic systems into Git-native workflows and CI pipelines so agents can build, test, and iterate against real GPUs.
- Contribute reusable agentic methodologies to cross-organizational collaborative groups.
Requirements
- Master's degree in Computer Science, Engineering, or equivalent experience.
- Five or more 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 and synchronizing at appropriate times.
- Experience building tools or systems shaped by direct partnership with internal customer or user teams.
- Experience 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, such as LangChain or 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).
Preferred Qualifications
- Passion for following the evolution of ML hardware and staying current with 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 amplify engineering teams.
- Ability to thrive in ambiguous, self-directed environments while communicating clearly, actively listening, and finding ground truth.
Compensation and Benefits
- Base salary range for Level 3: $152,000–$241,500 USD per year.
- Base salary range for Level 4: $184,000–$287,500 USD per year.
- Compensation is determined based on location, experience, and the pay of employees in similar positions.
- Eligible for equity and benefits.
- Applications will be accepted at least until October 16, 2026.
- NVIDIA is an equal opportunity employer committed to an inclusive work environment.
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