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Sobre esta vaga de Senior Software Engineer, World Model Systems Engineering - Autonomous Vehicles na NVIDIA

NVIDIA · Presencial · US, CA, Santa Clara

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re 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 a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.


The Automotive Vehicles team is searching for a creative and expert Systems Engineer to help us expand NVIDIA's autonomous vehicle solution. You will participate in a focused effort to productize ground-breaking perception solutions that will redefine the world of transportation and the growing field of self-driving cars. You will work with a world-wide team of systems engineers, AI applied researchers and software engineers in building world-class perception and mapping and localization solutions, while achieving or exceeding all NVIDIA and automotive standards & guidelines. You'll find the work is exciting, fun and relevant!


What you will be doing:

  • Develop perception, mapping and localization system requirements from use cases and customer requirements
  • Perform analysis to define performance requirements and allocate performance budgets
  • Plan validation and formulate test cases and critical metrics to ensure compliance with requirements
  • Test performance and triage results
  • Perform detailed analysis and develop dashboards
  • Work closely with hardware engineering, safety, developers and product teams to ensure we build the right product
  • Find innovative solutions to constantly improve our processes
  • Establish relationships with all relevant partners across the organization

What we need to see:

  • MS, or PhD in Engineering, Physics, Computer Science, Data Science or another related field (or equivalent experience)
  • 8+ years systems engineering experience on complex systems, systems analysis (e.g. error budget creation, modeling of scenarios), data analysis, and statistical analysis
  • Experience with verification and validation of world model stacks
  • Experience with VLMs, LLMs and agentic workflows for purposes of automated triage
  • Data strategy definition
  • Understanding of failure analysis methodologies such as Fault Tree Analysis (FTA) and MTBF decomposition
  • Strong leadership and interpersonal skills, with the ability to drive alignment across large organizations
  • Proficiency with SQL and Python or C++

Ways to stand out from the crowd:

  • Experience with autonomous vehicles and/or machine learning, computer vision
  • You have experience working with large datasets, data science, analytics
  • Software engineering experience  

#AutonomousVehicles

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 2, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

#deeplearning
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