About Rivian Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. Role Summary 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. 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 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. 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. Pay Disclosure Salary Range for California Based Applicants: $179,000 - $285,000 (actual compensation will be determined based on experience, location, and other factors permitted by law). Benefits Summary: Rivian provides robust medical/Rx, dental and vision insurance packages for full- time employees, their spouse or domestic partner, and children up to age 26. Coverage is effective on the first day of employment, and Rivian overs most of the premiums. Equal Opportunity Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information or characteristics, physical or mental disability, marital/domestic partner status, age, military/veteran status, medical condition, or any other characteristic protected by law. Rivian is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, such as those covered by the Americans with Disabilities Act, that requires accommodations to assist you in the search and application process, please email us at candidateaccommodations@rivian.com. 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Rivian may use your Candidate Personal Data for the purposes of (i) tracking interactions with our recruiting system; (ii) carrying out, analyzing and improving our application and recruitment process, including assessing you and your application and conducting employment, background and reference checks; (iii) establishing an employment relationship or entering into an employment contract with you; (iv) complying with our legal, regulatory and corporate governance obligations; (v) recordkeeping; (vi) ensuring network and information security and preventing fraud; and (vii) as otherwise required or permitted by applicable law. Rivian may share your Candidate Personal Data with (i) internal personnel who have a need to know such information in order to perform their duties, including individuals on our People Team, Finance, Legal, and the team(s) with the position(s) for which you are applying; (ii) Rivian affiliates; and (iii) Rivian’s service providers, including providers of background checks, staffing services, and cloud services. Rivian may transfer or store internationally your Candidate Personal Data, including to or in the United States, Canada, the United Kingdom, and the European Union and in the cloud, and this data may be subject to the laws and accessible to the courts, law enforcement and national security authorities of such jurisdictions. Please note that we are currently not accepting applications from third party application services.
Sr / Staff Machine Learning Engineer, Perception
Sr/Staff ML Engineer for Rivian's Perception Team, focusing on developing, deploying, and optimizing ML models for autonomous vehicle safety-critical features. Responsibilities include owning the ML model lifecycle, architecting data pipelines, developing evaluation tools, and optimizing system performance for real-time embedded deployment.
What you'd actually do
- 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.
Skills
Required
- Python
- PyTorch
- TensorFlow
- C++
- deep learning models
- autonomous vehicles
- robotics
- safety-critical systems
- perception models
- training infrastructure
- large-scale datasets
- distributed environments
- Vision foundation models
- temporal/spatial modeling
- attention/transformer architectures
- auto-labeling systems
- data augmentation
- Sensor signal decoding
- multi-modal sensor fusion
- pose/trajectory estimation
- action or intent recognition
- driver/passenger monitoring
- System and algorithmic optimization
- software engineering best practices
- empirical performance analysis
- communication
- collaboration
Nice to have
- cabin monitoring
- gaze estimation
- facial expression analysis
- action recognition
- auto-labeling tools
- cloud-based ML ops
- open-source contributions
What the JD emphasized
- safety-critical
- real-time embedded deployment
- production-ready perception models
- large, complex multi-modal datasets
- real-time embedded systems
Other signals
- deployment on real vehicles
- real-time embedded deployment
- production-ready perception models
- large, complex multi-modal datasets