Senior Systems Software Engineer, Machine Learning

NVIDIA • Durham, NC

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About The Position

NVIDIA has been transforming computer graphics, PC gaming, accelerated computing, and machine learning for more than 25 years. It’s a unique legacy of innovation fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing – an era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. We’re hiring a Deep Learning Engineer with strong experience in generative AI, LLMs/VLMs, computer vision, and agentic systems. If you’ve spent more time than you’d like to admit building workflows to populate data and/or diversify/expand your dataset, you’ll likely feel at home here. Bonus points if you’ve worked with 3D computer vision (extra bonus if you actually enjoyed it). The team is a balanced mix of engineers and scientists, and we care about both rigor and actually getting things out the door. The culture is collaborative, low-ego, and built around ownership. If you enjoy building systems that get used—and working with people who know when to debate and when to just run the experiment—this jobs is for you.

Requirements

• Master's, or preferably a PhD degree in Computer Science or a related field (or equivalent experience)
• 5+ years of experience
• Solid mathematical and algorithmic foundation and proven expertise demonstrated through research publications, internships, or significant project experience.
• Strong background in computer vision and deep learning.
• Excellent programming skills in Python and C/C++.
• Excellent software engineering fundamentals.
• Ability to develop code in Unix/Linux environments.
• Excellent written, visual, and verbal communication skills to present performance challenges, tradeoffs, and architectural alternatives.
• Strong collaboration skills to partner with other teams.

Nice To Haves

• Experience designing and operating multi-agent pipelines in production, including handling non-deterministic failures, retry logic, and tool-call error recovery
• Shipped a product feature backed by a VLM (e.g., image captioning, document understanding) — including handling inference latency, cost-per-call tradeoffs, and degraded-mode fallbacks
• Shipped AI-powered features to real users — not just prototyped with agent frameworks. If your experience is primarily personal projects or hackathons, this role may not be the right fit yet.

Responsibilities

• Convert research into real products (not just slide decks or notebooks)
• Help build workflows that diversify datasets and/or populate data
• Ship machine learning workflows/pipelines fast and iterate faster
• Leverage LLM/VLM and agents in the data generation pipeline
• Define evaluation criteria and run offline evals before any model or prompt change reaches production

Benefits

• equity
• benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Senior