Director Product Legal – Physical Ai jobs at Nvidia

What You'll Be Doing:

• Working as a key member of our cloud solutions team, you will be the go-to technical expert on NVIDIA's products, helping our clients architect and optimize GPU solutions for AI services.

• Collaborating directly with engineering teams to secure design wins, address challenges, usher projects into production, and offer support through the project's lifecycle.

• Acting as a trusted advisor to our clients, while developing reference architectures and best practices for running Microsoft AI workloads on NVIDIA infrastructure.

What We Need To See:

• 4+ years of experience in cloud computing and/or large-scale AI systems.

• A BS in EE, CS, Math, or Physics, or equivalent experience.

• A proven understanding of cloud computing and large-scale computing systems.

• Proficiency in Python, C, or C++ and experience with AI frameworks like Pytorch or TensorFlow.

• Passion for machine learning and AI, and the drive to continually learn and apply new technologies.

• Excellent interpersonal skills, including the ability to explain complex technical topics to non-experts.

Ways To Stand Out From The Crowd:

• Recent projects or contributions (for example, on GitHub) related to large language models and transformer architectures.

• Knowledge of Azure cloud and AzureML services.

• Experience with CUDA programming and optimization.

• Familiarity with NVIDIA networking technologies such as Infiniband.

• Proficiency in Linux, Windows Subsystem for Linux, and Windows.

What you'll be doing:

• Design, build and optimize agentic AI systems for the CUDA ecosystem.

• Co-design agentic system solutions with software, hardware and algorithm teams; influence and adopt new capabilities as they become available.

• Develop reproducible, high-fidelity evaluation frameworks covering performance, quality and developer productivity.

• Collaborate across the AI stack—from hardware throughcompilers/toolchains,kernels/libraries, frameworks, distributed training, andinference/serving—andwith model/agent teams.

What we need to see:

• Bachelor’s degree in Computer Science, Electrical Engineering, or related field (or equivalent experience); MS or PhD preferred.

• 3 years+ industry or academia experience with AI systems development; exposure to building foundational models, agents or orchestration frameworks; hands-on experience with deep learning frameworks and modern inference stacks.

• Strong C/C++ and Python programming skills; solid software engineering fundamentals.

• Experience with GPU programming and performance optimization (CUDA or equivalent).

Ways To Stand Out From The Crowd:

• Strong experience in building/evaluating deep learning models, coding agents and developer tooling.

• Demonstrated ability to optimize and deploy high-performance models, including on resource-constrained platforms.

• Demonstrated ability in GPU performance optimizations, evidenced by benchmark wins or published results.

• Publications or open-source leadership in deep learning, multi-agent systems, reinforcement learning, or AI systems; contributions to widely used repos or standards.

What you'll be doing:

• Define and Prove: Collect insights and build market analysis that provides the data to enter or change new markets through a clear value proposition for physical AI reasoning capabilities

• Market intelligence: Understand robotic, autonomous vehicle, and industrial automation developers, partners, and the physical AI ecosystem

• Build and Deliver: Collaborate with research and engineering to promote the customer's needs through setting feature priorities across roadmaps

• Sense and Respond: Work closely with customers, build surveys, present at conferences, understand product quality and supervise critical features, improvements, and bugs.

• Marketing content creation: Work with marketing to define positioning that enables the creation of technical content, including blog posts, webinars, developer tutorials, and more to communicate the product value proposition

• Product launches: Define the go-to-market strategy and provide guidance to the cross-functional implementation of the plan across marketing, public relations, and sales.

What we need to see:

• BS or MS in Computer Science, Engineering, or other technical field (or equivalent experience).

• 12+ years of product management, or similar, experience at a technology company

• Passionate about working at the intersection of ground breaking research and practical product development, driving teams to translate breakthrough innovations into shippable software, services, libraries, and SDKs.

• Proven track record of collaboration across teams, customers, and partners

• Extensive experience working in highly matrixed environments, able to lead through influence and corral and align many teams on a common vision

• Outstanding interpersonal and public communication skills with a shown ability to articulate a value proposition to technical and non-technical audiences.

Ways to stand out from the crowd:

• Experience working with multimodal Large Language Models, specifically with curating data, training or fine-tuning, and using reinforcement learning

• Thorough understanding of physics, robotics and/or autonomous vehicles

• Extensive knowledge of 3D graphics, computer vision, spatial computing pipelines, and related tools/ecosystems (e.g., game engines, VFX tools, simulation platforms)

• Background in motion capture, virtual production, or real-time rendering workflows

What you’ll be doing:

• Lead positioning, messaging and launches for key data science and processing products

• Develop technical content such as blogs, videos, and presentations to help data scientists embrace NVIDIA’s SDKs

• Support go-to-market for NVIDIA hardware platforms for data processing as part of cross-functional team

• Conduct competitive and ecosystem analysis to develop positioning and focus promotion

• Engage with the data science community directly and present key technologies at events

• Derive insights from customer adoption trends and share with internal teams

What we need to see:

• 8+ years’ experience with launching, go-to-market and scaling technical product adoption for data scientists or developers.

• Bachelors degree in a scientific or technical field (or equivalent experience).

• Knowledge of data science packages and machine learning frameworks.

• You love and thrive in a cross-functional organization, by collaborating with peers across teams and functions.

• Excellent written and spoken communication skills.

• Experience presenting at meetings, conferences, and webinars.

• Strong grasp of content marketing and social media methodologies.

• Comfortable reviewing Python code used in presentations, demos, and articles.

Ways to stand out from the crowd:

• Clearly communicate your understanding of NVIDIA’s strategy and technology.

• Measurable impact (metrics, awards, key accomplishments) you can share.

• Growing portfolio of blogs and social posts with links to your relevant work, including technical writing, social media presence, videos, and research papers.

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.

What you'll be doing:

• Own and complete the building and development of reporting solutions across SFDC, Wrike, Power BI, and related platforms based on field and management needs.

• Lead needs assessments and requirements analyses to identify efficient tools and processes for business reporting.

• Build, analyze, and present data-driven insights and forecasts, offering actionable recommendations to collaborators.

• Develop and maintain NVIS dashboards that deliver clarity and performance visibility across key operational metrics.

• Collaborate with cross-functional teams—engineering, operations, planning, OEM partners, and logistics—to document and optimize lifecycle processes.

• Serve as the primary contact for Data Analytics process blocking issues for lifecycle accountability, ensuring efficient and transparent issue resolution.

• Monitor operational performance, document workflows, and drive continuous improvement initiatives across data and logistics functions.

What we need to see:

• Bachelor’s degree or equivalent experience.

• 5+ years of experience overall, with at least 4 years in data reporting, process mapping, supply chain, logistics, or related program management.

• Verified background in building dashboards and analytics through SFDC, Wrike, Power BI, or related tools.

• Strong understanding of information systems integration, reporting methodologies, and data visualization guidelines.

• Excellent analytical, problem-solving, and communication skills for cross-functional collaboration.

• Demonstrated ability to detail and refine processes, manage reporting tasks, and synthesize insights for executive audiences.