Senior AI Systems Engineer: Agentic Workflows

NVIDIA

Santa Clara (CA)

On-site

USD 184,000 - 357,000

Full time

10 days ago
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Job summary

NVIDIA is seeking a highly capable applied ML engineer to join the Agentic Engineering team within the Deep Learning Framework Group in Santa Clara. You will craft agentic workflows that automate code generation, testing, and tuning across NVIDIA’s frameworks and developer tools, enabling autonomous, high-dimensional optimization on real hardware.

The role emphasizes collaboration with internal teams, architectural judgment, and shipping complex projects with minimal direction, supported by

Qualifications

  • 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.

Responsibilities

  • Develop a deep, shared understanding with early-adopter teams and identify friction points for agentic workflows.
  • Iterate with teams on proof points to validate or revise plans.
  • Architect durable agentic solutions spanning compiler infrastructure, multi-agent orchestration, and GPU-backed workflows.
  • Integrate systems into git-native workflows and CI pipelines for build, test, and iteration on real GPUs.

Skills

Python
GPU experience
AI systems
Project leadership

Education

MS in Computer Science or Engineering

Tools

LangChain
LangGraph

Job description

NVIDIA is seeking a highly capable applied ML engineer to join the Agentic Engineering team within the Deep Learning Framework Group in Santa Clara. You will craft agentic workflows that automate code generation, testing, and tuning across NVIDIA’s frameworks and developer tools, enabling autonomous, high-dimensional optimization on real hardware.

The role emphasizes collaboration with internal teams, architectural judgment, and shipping complex projects with minimal direction, supported by

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