Senior Software Engineer, Developer Experience

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior Software Engineer focused on integrating Agentic AI and ML into the software development lifecycle to create an intelligent ecosystem for developers, predicting failures, automating triage, and improving feedback loops. This involves building predictive models, AI agents for build/test selection, and LLM-based solutions for workflow automation.

What you'd actually do

  1. Design and implement a solution for a high-signal, rapid-feedback loop to ensure developers can validate changes with speed and confidence.
  2. Implement tools to measure and report on improvements in key metrics like cycle time, change failure rate, and mean time to recovery (MTTR).
  3. Build and deploy predictive models to identify high-risk commits, forecast potential build failures, and flag changes that have a high probability of failures.
  4. Design and Develop AI agents to select change centric builds and tests.
  5. Design and implement AI-driven solutions across software development lifecycles to identify and eliminate systemic friction in the engineering workflow, with a primary focus on reducing waste in the CI pipelines.

Skills

Required

  • BS/MS in Computer Science or related field (or equivalent experience)
  • 5 + years of proven experience building or supporting large software projects or equivalent
  • Hands-on experience on Python/Java/Go
  • Experience in working with SQL/NoSQL database systems such as MySQL, MongoDB or Elasticsearch.
  • Experience with tools for CI/CD setup such as Jenkins, Gitlab CI, Packer, Terraform, Artifactory, Ansible, Chef or similar tools.
  • Agentic frameworks and tools
  • Knowledge of build tools like Make, Maven or Ant.
  • Excellent data analysis skills and demonstrated ability to solve complex issues involving multiple software or hardware components.
  • Expertise in service oriented architecture and RESTful APIs.
  • Strong collaborative and interpersonal skills.

Nice to have

  • Deep practical knowledge of Large Language Models (LLMs), Machine Learning (ML), Agent development, MCP.
  • Hands-on experience implementing AI solutions to solve real-world developer productivity problems.
  • Research emerging AI technologies and engineering best practices to continuously evolve our development ecosystem and maintain a competitive edge.
  • Knowledge in setting up and maintaining systems monitoring and logging tools.

What the JD emphasized

  • Agentic frameworks and tools
  • Deep practical knowledge of Large Language Models (LLMs), Machine Learning (ML), Agent development, MCP.
  • Hands-on experience implementing AI solutions to solve real-world developer productivity problems.

Other signals

  • Integrate Agentic AI and Machine Learning into the software development lifecycle
  • Build predictive models to identify high-risk commits and forecast potential build failures
  • Design and Develop AI agents to select change centric builds and tests
  • Leverage large language models (LLMs) and Agentic AI to automate complex engineering workflows