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NVIDIA

Designs the chips and software that power AI

Principal OEM Execution Lead - Autonomous Driving Solutions

Program ManagementConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A BS or MS in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related field, or equivalent experience.
  • At least 15 years of relevant experience in automotive software, autonomous driving, embedded systems, vehicle platforms, or another complex safety-critical product domain, including significant technical and organizational leadership.
  • Demonstrated success taking a complex automotive software product from requirements and architecture through integration, validation, start of production, and production support with a major OEM.
  • Strong technical understanding of system and software architecture, embedded compute, vehicle networks, sensor and actuator interfaces, diagnostics, software release processes, and system-level validation.
  • Proven ability to lead large multi-functional programs in a matrixed organization and resolve ambiguous, high-impact issues spanning organizational boundaries.
  • Working knowledge of automotive development and compliance practices such as Automotive SPICE, ISO 26262, ISO/SAE 21434, SOTIF, cybersecurity, change control, and production-quality processes.
  • Strong written and verbal communication skills, along with customer-facing leadership and the judgment to balance OEM priorities, product integrity, safety, schedule, quality, and long-term platform scalability.
  • Willingness to travel to OEM and engineering sites as program needs require.

Responsibilities

  • Lead NDAS execution with major OEMs across the full program lifecycle, from feature definition and technical alignment through vehicle integration, validation, launch, and production ramp, owning the coordinated engineering plan for each vehicle line, including requirements, architecture, adaptation scope, achievements, staffing, validation strategy, release criteria, and production-readiness gates.
  • Translate OEM vehicle requirements and use cases into clear commitments for NVIDIA teams, ensure OEM partners understand product capabilities, constraints, assumptions, and their required work, and build scalable, repeatable workflows for adapting NDAS to specific OEM platforms, vehicle architectures, sensor configurations, compute platforms, networks, and development processes.
  • Drive multi-functional implementation across autonomous-driving features, systems, platform software, vehicle integration, functional safety, cybersecurity, validation, quality, release, and field engineering.
  • Establish an operating cadence with actionable achievements, objective readiness metrics, risk and dependency management, decision logs, and clear paths for addressing customer concerns that block progress.
  • Direct technical issue resolution across systems, safety, security, and development groups by determining root causes, clarifying ownership, surfacing tradeoffs, and ensuring lasting closure without slowing program progress.
  • Represent overall program health and technical readiness in reviews with OEM engineering leaders and NVIDIA executives, communicating decisions, risks, recovery plans, and resource needs.
  • Ensure lessons from each vehicle program feed back into the product, tooling, documentation, test infrastructure, and integration model to improve subsequent OEM programs.
  • Develop leaders and strengthen a culture of ownership, transparency, engineering rigor, and customer dedication across the extended program team.

Desired Qualifications

  • Direct leadership experience launching L2+/L3 autonomous-driving or advanced driver-assistance functions on multiple vehicle lines.
  • Experience with centralized vehicle compute, DRIVE OS, AUTOSAR, real-time operating systems, middleware, sensor integration, data collection, simulation, SIL/HIL/VIL, and fleet validation.
  • A record of converting customer-specific integration work into reusable platform capabilities and scalable engineering workflows.
  • Experience leading distributed engineering organizations and joint execution models across North America, Europe, and Asia.
  • Ability to translate an executive decision into the corresponding system behavior, logs, interfaces, test evidence, and corrective action required to resolve the issue.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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