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NVIDIA

Designs the chips and software that power AI

Senior Data and AI Solutions Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listedIn-person

About the Job

Requirements

  • A B.Sc. or M.Sc. in Computer Science or a related field, or equivalent experience.
  • At least 12 years of experience building and deploying production software, data products, internal tools, or engineering productivity platforms, and at least 2 years building AI-enabled or agentic systems, including tools and skills, retrieval-augmented generation pipelines, persistent memory, and evaluation infrastructure.
  • Hands-on development experience with Python, full-stack software development, SQL and NoSQL databases, cloud environments, and internal tool development.
  • Extensive experience utilizing coding agents for development.
  • Ability to decipher complex domains and deliver pragmatic, production-grade AI and data solutions that drive measurable engineering productivity.
  • Ability to take ownership of the lifecycle from data debugging to cross-functional leadership.
  • Ability to collaborate with cross-functional partners, build positive working relationships, and communicate complex concepts clearly to technical and business audiences.
  • Commitment to continuous learning and development.

Responsibilities

  • Work with engineers, managers, and cross-functional teams to understand complex engineering domains, identify bottlenecks, and build solutions that improve decision-making, execution speed, and operational quality.
  • Develop and deliver production-grade tools, automated workflows, and agentic AI solutions from concept through deployment.
  • Run proof-of-concepts on emerging AI and agentic technologies and productize successful ones.
  • Implement data flywheels that improve quality through telemetry, benchmarking, automated evaluation, and structured feedback loops.
  • Contribute ideas and code to Product Engineering’s evolving data infrastructure.
  • Improve data quality, integrity, governance, metric definitions, and usability across engineering domains.
  • Train and enable engineers, managers, and stakeholders to use data, business intelligence, and AI tools effectively.

Desired Qualifications

  • Background in product engineering, hardware engineering, networking, semiconductors, or complex engineering organizations.
  • Meaningful open-source contributions, such as core commits, maintainership, widely adopted libraries, or public technical artifacts demonstrating system-level depth.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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