Sr Staff Machine Learning Engineer Jobs in California

INTRODUCTION

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.

ROLE AND RESPONSIBILITIES

• 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.

BASIC QUALIFICATIONS

• 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.

PREFERRED QUALIFICATIONS

• 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.