At NVIDIA, we're not just building the future, we're generating it! Our Cosmos generative AI engineering team is pushing the boundaries of what’s possible across multimodal learning, video generation, synthetic data, intelligent simulation, and agentic systems. We are looking for exceptionally driven engineers and applied scientists with deep experience in generative modeling to help define the next era of AI computing.
What you'll be doing:
You will own and evolve the Cosmos open-source and internal research codebases, crafting core infrastructure that supports our foundation model research and deployment.
Refactor and modularize large research-driven code into clean, testable, maintainable libraries for use across teams.
Integrate and adapt off-the-shelf models into our pipelines as preprocessors, postprocessors, or evaluation components.
Build model-serving endpoints (e.g., with Gradio or FastAPI) to enable researchers and internal users to experiment with models interactively.
Design, implement, and maintain evaluation pipelines, providing high-quality tooling to the broader team to measure model quality and track improvements.
Improve configuration hygiene and reproducibility using systems like Hydra, and ensure smooth overrides, templates, and environment switching.
Lead efforts in packaging and release of Python modules using modern tools (uv, just, pydantic) for both OSS and internal consumption.
Set the standard for code health, test coverage, and release readiness across the team. Write documentation and automation to scale good practices.
What we need to see:
Expert-level proficiency in Python, with a strong foundation in modular design, abstraction boundaries, and collaborative codebase evolution.
Fluency with PyTorch, including the ability to run, debug, and patch inference-time model behavior in research-level codebases. Comfort modifying pre/post-processors, model wrappers, and checkpoint logic.
Proven experience in refactoring large codebases—cleaning up legacy implementations, eliminating anti-patterns, and paying down tech debt to improve long-term maintainability.
Strong grasp of configuration systems, especially Hydra, with an emphasis on reproducibility, override logic, and environment scoping.
Familiarity with Python packaging tools like uv, just, and pydantic, including experience managing environment consistency and shipping libraries as artifacts.
Strong instincts around code health: API design, directory structure, writing unit and integration tests, exception hygiene, docstrings, and dependency isolation.
Comfortable deploying models internally via Gradio or similar frameworks to enable interactive evaluation and feedback from researchers or downstream users.
BS or MS (or equivalent experience) in Computer Science, Software Engineering, or a related technical field and 10+ years of industry experience.
Ways to stand out from the crowd:
Proficiency in model configs, especially Hydra! Comfortable crafting hierarchical config systems with reusable templates, environment scoping, and overrides for evaluation, inference, or release.
Prior work cleaning up sophisticated generative model codebases—adding tests, improving wrappers, and instrumenting code for observability and debugging.
Demonstrated success raising engineering quality in a research setting: taking exploratory code and evolving it into a robust, production-friendly module.
Track record of mentoring teammates on software engineering best practices and proactively identifying long-term structural risks in fast-moving teams.
Passion for building ML tooling that is not only functional, but also elegant, intuitive, and maintainable by others.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Similar Jobs
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
