As a Sr. Design Engineer within Rivian’s Sensing Systems Team, you will be a core contributor to the architecture, development, deployment, and optimization of advanced sensing systems driving safety-critical, customer-facing features for Rivian’s autonomous vehicles. This role is deeply technical and hands-on, requiring a strong background in hardware systems engineering, sensor bring-up, vehicle integration, and cross-domain debugging. You will be responsible for ensuring sensors function reliably and meet system-level performance targets under real-world operating conditions.
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Contribute to hardware integration and validation of all ADAS sensors (camera, radar, LiDAR) into prototype and production vehicle platforms.
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Own the sensor SW interface definition, including ethernet / MIPI / CAN frame formats and PTP timesync.
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Own the sensor FW development and sensor bring-up, including functional validation and failure analysis.
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Work closely with perception, controls, and embedded software teams to ensure sensor data integrity and system alignment.
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Develop and execute system-level test plans, including lab validation, vehicle-level testing, and environmental stress screening (thermal, vibration, EMI/EMC).
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Implement and refine sensor calibration and alignment processes, including static and dynamic calibration for multi-modal fusion readiness.
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Drive issue triage and resolution across HW, FW, and mechanical domains with a rigorous, data-driven approach.
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Support sensor supplier selection, evaluation, and bring-up, including root cause analysis on field failures or yield issues.
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Maintain ownership of integration documentation, test artifacts, and interface control specifications.
Required:
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Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, or a related field.
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Expert proficiency with Python, strong C++ skills for performance-critical, production code.
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System and algorithmic optimization, robust software engineering best practices, and empirical performance analysis.
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Understanding of signal interfaces and protocols: CAN, Ethernet, SPI, MIPI, and synchronization methods (PTP, PPS).
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Familiarity with test equipment (oscilloscopes, logic analyzers, power analyzers), as well as vehicle data loggers and network analyzers.
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Familiarity with sensor fusion algorithms and the impact of physical alignment, timing, and data integrity.
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Working knowledge of sensor modeling and simulation tools.
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Highly effective communicator and team collaborator; demonstrated ability to partner across technical specialties and organizational boundaries to deliver end-to-end solutions.
Preferred:
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Hands-on experience integrating camera, radar, and LiDAR sensors in automotive or robotics platforms.
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Familiarity with one or more deep learning frameworks (e.g., PyTorch, TensorFlow)
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Experience with vehicle integration and on-road validation, including structured debug of real-world sensor performance issues.
