NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU serves as the intelligence behind computers, robots, and autonomous vehicles that perceive the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. We pioneered a supercharged form of computing loved by the most demanding computer users in the world - scientists, designers, artists, and gamers. It’s not just technology though! It is our people, some of the brightest in the world, and our diverse company culture that make NVIDIA one of the most fun, innovative and dynamic places to work in the world! At the center of NVIDIA's culture are our core values like innovation, excellence and determination and team, that guide us to be the best we can be. As an NVIDIAN, you’ll be immersed in a diverse, encouraging environment where everyone is motivated to perform at their highest level. Come join the team and see how we can make a lasting impact on the world.
What you’ll be doing:
Develop test strategy and use cases/scenarios for L3 and L4 autonomous driving products based on customer needs, traffic regulations, certification standards, and industry guidelines.
Define test strategy and execution plan on vehicle platform.
Formulate test cases and define critical performance metrics to ensure compliance with functional and safety requirements.
Close collaboration with different stakeholders to define the KPIs and evaluate the product progress by closing the loop with test data/results.
Establish strong multi-functional relationships with collaborators across engineering, safety, and product teams.
Scale and innovate the product ODDs by designing campaigns and roadmap.
Lead L3/L4 vehicle integration and retrofitting, managing end-to-end processes to advance testing and validation schedules.
Spearhead data triage and root cause analysis, driving issue resolution and generating comprehensive test reports.
Promote and cultivate a strong systems engineering approach across the organization.
What we need to see:
MS or PhD in Engineering, Physics, Computer Science, or a related field (or equivalent experience).
10+ years proven experience in ADAS, system analysis, data analysis, and software architecture.
3+ years of experience driving the mass-production launch of autonomous driving products, including supervised FSD, urban NOA, or equivalent features.
Proficiency in programming languages commonly used in AV development and testing (e.g., Python, C++) and familiarity with data query and analysis tools.
Awareness of functional safety (ISO 26262), Safety of the Intended Functionality (SOTIF / ISO 21448), and V-model validation methodologies.
Strong leadership and interpersonal skills, with the ability to drive alignment across large organizations.
Proven understanding of trade-offs between End-to-End deep learning approaches, classical modular perception/planning stacks, and associated validation and test strategies.
Deep understanding of autonomous vehicle sensor suites (LiDAR, Radar, Camera, Ultrasonic) and their specific failure modes, edge cases, and validation requirements.
Provide technical mentorship to junior engineers and lead by example in establishing rigorous testing and validation standards.
Ways to stand out from the crowd:
Experience contributing to a launched L3/L4 autonomous vehicle program.
Hands-on experience with testing strategy for ADAS, large-scale datasets, data analytics workflows.
Innovation by tools/out of the box thinking to resolve the problem statement.
Passionate and self-motivated about autonomous technology.
Skills Required
- MS or PhD in Engineering, Physics, Computer Science, or related field (or equivalent experience)
- 10+ years proven experience in ADAS, system analysis, data analysis, and software architecture
- 3+ years driving mass-production launch of autonomous driving products
- Proficiency in programming languages commonly used in AV development and testing (e.g., Python, C++)
- Awareness of functional safety (ISO 26262) and validation methodologies
- Strong leadership and interpersonal skills
- Proven understanding of trade-offs between End-to-End deep learning approaches and classical modular stacks
- Deep understanding of autonomous vehicle sensor suites and validation requirements
- Provide technical mentorship to junior engineers
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
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.”








