Senior Software Engineer, Agentic Engineering
$224k - $357k • Remote • US, CA, Santa Clara
Posted 7d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Agent Engineer
About the job
Join the new Agentic Engineering team, focused on automating code generation, testing, and tuning across NVIDIA's frameworks, compilers, and developer tooling. This is a greenfield opportunity offering foundational technical influence within a high-autonomy team. You will partner directly with early-adopter teams to translate complex requirements into durable, scalable infrastructure, applying modern AI to the craft of engineering itself. The role involves identifying friction points where agentic workflows can have the highest impact and iterating with teams as they integrate agents into production.
Responsibilities
- Develop a deep understanding of customer needs to identify high-impact areas for agentic workflows.
- Iterate with customer teams on proof points to validate and revise agent integration plans.
- Agent-ify compiler infrastructure for autonomous optimization with closed-loop validation.
- Implement multi-agent orchestration using LLM-native tooling or frameworks like LangChain/LangGraph.
- Drive autonomous loops for applying changes, measuring results, and iterating.
- Integrate agent systems into git-native workflows and CI pipelines for building, testing, and iterating against GPUs.
- Contribute to cross-organizational efforts to share and adopt reusable agentic methodologies.
Requirements
- MS in Computer Science, Engineering, or equivalent experience.
- 12+ years of experience.
- Strong Python development skills.
- Working knowledge of GPUs or other highly data-parallel systems.
- Demonstrated experience using and supporting AI systems.
- Proven track record of shipping complex projects with minimal direction.
- Experience building tools or systems in partnership with internal customer or user teams.
- Ability to lead technical work through changing requirements and adapt direction based on evidence.
- Experience with multi-agent orchestration frameworks (e.g., LangChain, LangGraph) or LLM-based workflow automation.
- Experience with compiler infrastructure, intermediate representations, or program transformation.
- Experience with autonomous search or optimization over high-dimensional parameter spaces.
- Experience with hardware-aware performance optimization for deep learning workloads.
- Experience with code generation systems or domain-specific languages (DSLs).
Benefits
- Equity
- Benefits