We are now looking for a Senior Software Autonomous Vehicle Control Integration Engineer. As a Senior Software Control Integration Engineer, you will work on state-of-the-art autonomous vehicle technologies with leaders in AI / deep learning, computer vision, and vehicle control.
You will be responsible for developing, maintaining and integrating autonomous auto control algorithms and software. You will develop solutions for vehicle trajectory tracking, model predictive control, and vehicle state estimation. You should have strong analytical skills, good communication, and interpersonal capabilities to work in a team and make a joint effort towards a common goal. Prior knowledge about vehicle control, vehicle ECU, Drive-by-Wire systems, and in-vehicle network and autonomous vehicles are highly valued.
What you’ll be doing:
Contribute to the development related to vehicle dynamics and control and support other teams in such areas, e.g. ACC/LK, urban driving, trajectory planning, vehicle operation limit estimation, emergency maneuvers (collision avoidance, mitigation), etc.
Design technologies needed to build Autonomous Vehicle software stack, write new software modules from scratch that drive our vehicles.
Provide feedback for overall system design to product level: system architecture, failure mode, system redundancy, etc.
Build new performance offline and online benchmarks for vehicle control.
Define and build tools to tune our vehicle dynamics control and manage calibration updates for various vehicle types.
Drive integration efforts of control functionality across different platforms, support troubleshooting of control and actuation issues in vehicles.
Develop and maintain vehicle test suites to detect control integration problems, develop and maintain automated tests to ensure SW quality and prevent regressions.
Most importantly, work on groundbreaking and innovative technology, tackle difficult problems, and help build a world-class system!
What we need to see:
BS, MS, or higher degree in Mechanical Engineering, Electrical Engineering, Computer Science or equivalent experience
8+ years of work experience in relevant fields
Knowledge and hands-on application background in control theories, including at least three of the following fields: classical control, modern control, nonlinear control, MPC, optimal control, robust control, sliding mode control.
Understanding of state estimation techniques such as Luenberger observer, kalman filter, etc.
Ability to develop vehicle models and perform parameter identification and benchmarking.
Knowledge of high-level algorithm design and prototyping in simulation environments (Matlab/Simulink/Python) to product oriented implementation with C/C++.
Experience in architectural design and software development with C/C++ for real-time embedded control systems, such as vehicle ECU systems.
Understanding of test and verification methodologies for automotive software, know-how in writing unit and system level tests.
Experience of integrating, tuning, and validating prototype and/or production control software with application, driver, and vehicle network layers.
Experience of development and/or usage of in-vehicle control software verification and calibration tools for autonomous vehicle applications.
Ways to stand out from the crowd:
Hands-on experience in autonomous vehicle or advanced driver assist system development such as lane keeping, adaptive cruise control, etc.
Background in automotive safety concept, failure mode, common analysis tools such as FMEA
Experience in developing and launching vehicle safety critical control system products such as powertrain, steering system, brake system, advanced driver assist system, etc.
Understanding of deep learning and neural network concepts
12+ years engineering experience at automotive OEM, tier-1 supplier or autonomous driving start-ups
PhD with relevant experience
Academic and commercial groups around the world are powering a revolution in artificial intelligence using deep learning techniques running on NVIDIA GPUs, enabling breakthroughs in problems from image classification to speech recognition to natural language processing. Intelligent machines powered by AI computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error. This is truly an extraordinary time. The era of AI has begun.
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.
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.Skills Required
- Bachelor’s, master’s, or higher degree in Mechanical Engineering, Electrical Engineering, Computer Science, or equivalent experience
- 8+ years of relevant work experience
- Hands-on knowledge of control theory, including at least three areas such as classical, modern, nonlinear, MPC, optimal, robust, or sliding mode control
- Understanding of state estimation techniques, including Luenberger observers and Kalman filters
- Ability to develop vehicle models, perform parameter identification, and conduct benchmarking
- Experience designing and prototyping algorithms in MATLAB, Simulink, or Python and implementing them in C/C++
- Experience with architectural design and C/C++ software development for real-time embedded control systems and vehicle ECUs
- Understanding of automotive software testing and verification, including unit and system-level tests
- Experience integrating, tuning, and validating control software across application, driver, and vehicle network layers
- Experience developing or using control software verification and calibration tools for autonomous vehicle applications
- Hands-on autonomous vehicle or advanced driver-assistance system development experience, including lane keeping or adaptive cruise control
- Automotive safety concept and failure mode analysis experience, including FMEA
- Experience developing and launching safety-critical automotive control systems
- Understanding of deep learning and neural network concepts
- 12+ years of engineering experience at an automotive OEM, Tier 1 supplier, or autonomous driving startup
- PhD with relevant experience
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.
-
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.
-
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.
-
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.”
