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Selected role

Sr / Staff Machine Learning Engineer, Perception

Rivian Palo Alto, CA, US

Senior Full-time

As a Sr./Staff ML Engineer within Rivian’s Perception Team, you will be a core contributor to the architecture, development, deployment, and optimization of advanced machine learning algorithms driving safety-critical, customer-facing features for Rivian’s autonomous vehicles. With a focus on onboard perception (including areas like object detection, sensor fusion, cabin or driver monitoring, and multi-modal state understanding), you will have full ownership over the lifecycle of key perception projects, collaborating closely with cross-disciplinary teams spanning autonomy, planning, simulation, and ML infrastructure. This role is based in Palo Alto, CA.

Independently own the design, development, testing, deployment, and maintenance of perception ML models and supporting software for autonomous vehicle applications—including both onboard and cloud environments.
Drive the creation and continuous improvement of production-ready perception models for real-time embedded deployment (object detection, tracking, segmentation, pose estimation, scene understanding, etc.), ensuring robustness, performance, and resilience.
Architect and build scalable data pipelines and training infrastructure to support ML model iteration with large, complex multi-modal datasets, including auto-labeling and data augmentation capabilities.
Develop tools and processes to evaluate and measure the performance and health of perception and/or cabin-monitoring systems, and ensure integration with downstream autonomy modules.
Analyze, debug, and optimize perception system performance, from offline metrics and simulation validation to live, in-vehicle operation, addressing limitations like manual labeling bandwidth, ground truth availability, and real-world heterogeneity.
Collaborate tightly with teams across machine learning, sensor systems, embedded platform, planning, infrastructure, and data engineering to deliver integrated, customer-impacting autonomous features.
Share technical direction, mentor junior engineers, publish internal guidance, and help shape Rivian’s technical roadmap in perception.
Stay abreast of state-of-the-art research in machine learning, computer vision, and autonomous driving; drive adoption of best practices and pioneer new approaches where appropriate.
BS, MS, or PhD in Computer Science, Robotics, Electrical/Mechanical/Aerospace Engineering, or a related technical field.
5+ years of experience (Sr.), or 7+ years (Staff), developing and deploying deep learning models for autonomous vehicles, robotics, or other safety-critical, real-time embedded systems.
Expert proficiency with Python and one or more deep learning frameworks (e.g., PyTorch, TensorFlow); strong C++ skills for performance-critical, production code.
Demonstrated experience architecting, training, and evaluating perception models (2D or 3D, including sequential models), with exposure to deployment on real vehicles and/or production robotic systems.
Track record in building or leveraging complex training infrastructure (cloud and/or cluster-based) and working with large-scale datasets in distributed environments.
Hands-on experience with several of the following:
- Vision foundation models, temporal/spatial modeling, attention/transformer architectures, auto-labeling systems, data augmentation for diverse sensor configurations.
- Sensor signal decoding (camera, radar, lidar), multi-modal sensor fusion, pose/trajectory estimation, action or intent recognition, and state-of-the-art driver/passenger monitoring.
- System and algorithmic optimization, robust software engineering best practices, and empirical performance analysis.
Highly effective communicator and team collaborator; demonstrated ability to partner across technical specialties and organizational boundaries to deliver end-to-end solutions.
Bonus: Prior work in cabin monitoring (e.g., gaze estimation, facial expression analysis, action recognition), experience building auto-labeling tools, cloud-based ML ops, or open-source contributions to perception research.

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