Researcher 2

Location: Silicon Valley

Job Schedule: Full-time Hybrid

Education Requirement: see below

We're currently looking for a Researcher in Autonomous Systems Integration & Data Infrastructure to join our team. As a member of the Autonomous Systems team, you will enable end-to-end autonomous vehicle research through sensor integration, calibration, data collection, synchronization, and software infrastructure development. You will work closely with perception researchers and software engineers to integrate emerging sensing technologies, build scalable data collection pipelines, and develop robust hardware-to-software interfaces that accelerate autonomous driving research and development.

Think you’ve got the internal drive to support the production of great vehicles and join a winning team?  Apply for this position and start the process to kick your career into high gear.

Whom We’re Looking For: 

Required Technical & Execution Skills:

• Sensor Integration: Lead hardware and software integration of camera, radar, lidar, GPS/IMU, and other sensing modalities into autonomous vehicle research platforms.

• Autonomous Systems Infrastructure: Develop and maintain software interfaces connecting hardware sensors to software stacks using ROS1, ROS2, CAN, Ethernet, C++, and Python.

• Data Flywheel Ownership: Own data collection, sensor validation, calibration, synchronization, data quality verification, and dataset generation processes supporting autonomous driving research.

• Rapid Prototyping & Evaluation: Evaluate and benchmark emerging sensing technologies through rapid hardware integration, testing, and software-based performance analysis.

• Hardware-in-the-Loop Development: Design, implement, and troubleshoot hardware-in-the-loop and real-world vehicle testing workflows to enable scalable autonomous driving experimentation.

Required Leadership & Collaboration Skills:

• Technical Ownership: Independently drive hardware integration, data collection, and infrastructure workstreams from planning through validation and deployment.

• Cross-Functional Collaboration: Work closely with perception researchers, software engineers, and vehicle integration teams to support autonomous driving research objectives.

• Stakeholder Management: Coordinate hardware evaluations, sensor deployments, and testing activities while maintaining strong relationships with internal and external partners.

Required Communication & Visibility Skills:

• Technical Reporting: Communicate hardware integration results, data collection findings, and infrastructure roadmaps through documentation, technical reviews, and team presentations.

• Data Quality Advocacy: Drive alignment around sensor calibration, synchronization, data quality, and validation best practices.

• Consistency: Provide clear project updates, technical documentation, and sprint planning contributions to support efficient project execution.

Required Qualifications:

• Education & Experience: At least a Master's degree in Mechanical, Electrical, or Software Engineering, Computer Science, Applied Mathematics, Robotics, or a related field, with 3+ years of relevant industry or research experience; or a PhD in a related field.

• Demonstrated experience integrating and validating multiple sensor modalities, including camera, lidar, radar, and localization sensors, within robotic or autonomous systems.

• Demonstrated experience integrating hardware sensors into software systems for robotics, autonomous systems, ADAS, or related applications.

• Experience with camera intrinsic and extrinsic calibration. Experience with camera-lidar, camera-radar, lidar-lidar, or multi-sensor calibration is preferred.

• Strong understanding of sensor synchronization techniques including hardware-triggered synchronization, timestamp management, and protocols such as PTP.

• Hands-on experience evaluating and benchmarking sensor hardware, including rapid prototyping, validation, and performance analysis of novel sensing technologies.

• Hands-on experience with GPS/IMU systems, localization infrastructure, and sensor synchronization workflows.

• Experience developing and debugging software on Linux-based systems.

• Strong programming skills in C++ and Python, with experience developing software using ROS1 and/or ROS2.

• Experience with CAN, Automotive ethernet CAN, automotive communication protocols, and sensor driver development.

• Experience collecting, cleaning, validating, and managing large-scale datasets for autonomous systems, robotics, or machine learning applications.

What You’ll Look Forward to at Nissan:

Career Growth and Continuous Learning Opportunities: Benefit from diverse career paths, cross-departmental moves, and innovative learning platforms. Enhance your skills through seminars, leadership training, and tuition reimbursement programs, all while playing a vital role in shaping the future of transportation. From day one, you'll have the support to tackle challenges and contribute to impactful solutions across our organization.

Rewards: Be supported with a Comprehensive Benefits Package, including medical, mental health, parental leave, retirement savings, and unique Nissan perks including discounts on lease vehicles as part of our Employee Lease Program and/or Vehicle Purchase Program (VPP).  For more information access our Nissan Benefits Overview Guide.

Built for Your Best: 

https://www.youtube.com/watch?v=PXuame0vwiw&list=PLki2k0CDxrPe8V6WmEeEdhj1fosRD-JQa

Santa Clara California United States of America

Salary Range:

Salary Range Estimate: Annual Salary: (Minimum to Maximum of  Salary Range noted here). This compensation range represents the minimum and maximum base salary rates at Nissan for jobs assigned to this particular grade level. Please note that it is uncommon for an employee to be placed at either end of the range.  Rather, an employee’s actual base salary generally may fall somewhere in between and reflect the employee’s unique skills, work experience, education, work location, and market norms.  Additionally, pay may be based on comparisons to the base salary rates of other employees with similar backgrounds working in comparable roles.