Jobs / United States / Nvidia Corporation

Nvidia Corporation

Technical Product Marketing Engineer, Robotics Simulation - New College Grad 2027

US, CA, Santa Clara Posted today

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Skills this job asks for

Product managementPythonMachine learningPyTorchLinuxGitC++

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Sponsorship verdict

Sponsorship possible

One solid signal, not two. Worth applying, and worth asking about sponsorship early.

  • Employer is on a government sponsor recordThe US Department of Labor certified 2,374 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first.
  • The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
  • No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area.
  • What Nvidia Corporation paid sponsored hires in similar roles7 certified filings for “Product Manager” (Marketing Managers) in CA: $180k–$217k, median $201k.
  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
Employer is on a government sponsor record
The US Department of Labor certified 2,374 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. Source: LCA disclosure data (US Department of Labor (OFLC)).
No salary bar for this route
H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
What Nvidia Corporation paid sponsored hires in similar roles
7 certified filings for “Product Manager” (Marketing Managers) in CA: $180k–$217k, median $201k. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).

US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).

A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.

About the role

Today, NVIDIA is 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. As an NVIDIAN, you’ll be immersed in an inclusive, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world! We are looking for a Technical Product Marketing Engineer to build robotics simulation workflows and demonstrate how AI models can solve robotics tasks. You will develop working examples, evaluate their behavior and performance, and turn your findings into practical resources that help developers understand and adopt NVIDIA robotics technologies. As part of NVIDIA’s Robotics Technical Marketing team, you will work with engineering, product management, and product marketing to connect technical capabilities with developer needs. You will own well-defined projects—from implementation and testing through demonstrations and technical content—with mentorship and technical review from experienced teammates. What you will be doing: • Develop and test robotics simulation w/ agentic workflows using NVIDIA platforms Isaac Sim and Isaac Lab, including configuring robot models, environments, sensors, and task scenarios. • Integrate AI models and robot policies into simulation workflows, connecting simulation inputs, model inference, and robot behavior. • Build reusable Python code and sample applications that demonstrate practical robotics use cases. • Evaluate task success, runtime performance, and failure cases through repeatable experiments; document configurations, results, and limitations. • Create demos, tutorials, technical blogs, and workflow guides that explain what you built, how it works, and how developers can reproduce the results. • Collaborate with engineering and product teams to troubleshoot integration issues, validate technical claims, and surface opportunities to improve the developer experience. • Present your work and contribute to technical demonstrations and educational materials for developers, partners, and customer-facing teams. What we need to see: • Pursuing or recently completed a BS or MS degree in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or a related technical field, or equivalent experience. • Strong Python programming fundamentals, including experience debugging, testing, and organizing code. • Hands-on academic, research, internship, or personal project experience with robotics or simulation. • Foundational understanding of linear algebra, coordinate transformations, and robotics concepts such as sensors, kinematics, or control. • Familiarity with machine learning fundamentals and practical experience running or evaluating models using a framework such as PyTorch. • Ability to investigate technical problems systematically and communicate results through clear writing and presentations. • Initiative in learning new tools, incorporating feedback, and following through on defined technical deliverables. • Relevant experience may come from coursework, capstone projects, research, internships, open-source contributions, or personal projects. Ways to stand out from the crowd: • Experience with NVIDIA Isaac Sim, Isaac Lab, or other robotics simulators. • A project applying learned policies or pre-trained AI models to robotics tasks, such as perception, manipulation, or navigation. • Exposure to reinforcement learning, imitation learning, synthetic data generation, or simulation-to-real evaluation. • Familiarity with Linux, Git, ROS 2, C++, or GPU-accelerated comp

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