Senior Generative AI Software Engineer at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, AIIndustries

Requirements

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, and 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

Responsibilities

Own and evolve the Cosmos open-source and internal research codebases, crafting core infrastructure that supports 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 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

Skills

Python

PyTorch

Generative AI

Modular Design

Refactoring

FastAPI

Gradio

Hydra

pydantic

Model Serving

Evaluation Pipelines

Packaging

Testing

Documentation

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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