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 Auto-labelling is a foundational pillar of the Autonomy stack. In this Senior ML Engineer role, you will play a key role in delivering high-quality, scalable auto-labeling models. This includes training, optimizing and shipping auto-labeling models in the Autonomy stack. Use cases include mapping, lanes auto-labelling, object auto-labelling as well as other critical applications. You will ship production-grade models that push the boundaries of what’s possible. As such, you will also contribute to the whole end-to-end ML lifecycle & data flywheel of this effort: data acquisition, metrics definition, evaluation, model performance optimization, feedback loop. A key part of the role is especially dedicated to lidar-free auto-labeling, i.e. ship auto-labeling models that do not require lidar data. Responsibilities Deliver prod-grade, high-quality, scalable auto-labeling models. Use cases include AV mapping, lanes auto-labelling and/or object auto-labelling, among other critical applications. Train, optimize, ship auto-labeling models in the Autonomy stack, and continuously improve their performance. Deliver auto-labeling with and without lidar data. Establish rigorous evaluation and monitoring benchmarks. Identify and root-cause top-tier system anomalies, prioritizing high-impact optimizations to continuously push the needle on performance. Partner closely with the Autonomy group to ensure we meet the feature requirements Qualifications Education: BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a highly related quantitative field. Experience: 5+ years of professional experience building and scaling ML solutions, with a strong focus on the following: AV auto-labeling system at scale: Proven track record of hands-on experience delivering auto-labeling models for Autonomous Vehicles at scale. Auto labeling for mapping, lanes auto-labelling and/or object auto labelling. Perception stack: solid understanding of the AV perception stack. System engineering: Strong proficiency in Python alongside a solid understanding of modern Perception pipelines, benchmarking tools, and infrastructure. Execution: Demonstrated ability to drive progress across a complex, multi-domain system, in a fast-paced environment. Preferred Qualifications Experience in one of the following auto-labeling applications: mapping, lanes auto-labelling or object auto-labelling. Experience in Lidar-free auto-labeling Experience in mapping, especially from multiple vehicle passes and/or lidar-free mapping. Experience in complex,multi-modal, large-scale data flywheel Experience with multiple modalities (e.g., cameras, LiDAR, Radar). Experience with onboard edge deployment, cloud inference architectures, and balancing compute/efficiency trade-offs Pay Disclosure Salary Range for California Based Applicants: $179,000 - $223,000 (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. 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Senior ML Engineer, Perception
Senior ML Engineer focused on developing and scaling auto-labeling models for autonomous vehicles, including lidar-free applications. The role involves the full ML lifecycle from data acquisition to model optimization and deployment, with a strong emphasis on evaluation and system performance.
What you'd actually do
- Deliver prod-grade, high-quality, scalable auto-labeling models.
- Train, optimize, ship auto-labeling models in the Autonomy stack, and continuously improve their performance.
- Deliver auto-labeling with and without lidar data.
- Establish rigorous evaluation and monitoring benchmarks.
- Identify and root-cause top-tier system anomalies, prioritizing high-impact optimizations to continuously push the needle on performance.
Skills
Required
- BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a highly related quantitative field.
- 5+ years of professional experience building and scaling ML solutions
- Proven track record of hands-on experience delivering auto-labeling models for Autonomous Vehicles at scale.
- solid understanding of the AV perception stack.
- Strong proficiency in Python
- solid understanding of modern Perception pipelines, benchmarking tools, and infrastructure.
- Demonstrated ability to drive progress across a complex, multi-domain system, in a fast-paced environment.
Nice to have
- Experience in one of the following auto-labeling applications: mapping, lanes auto-labelling or object auto-labelling.
- Experience in Lidar-free auto-labeling
- Experience in mapping, especially from multiple vehicle passes and/or lidar-free mapping.
- Experience in complex,multi-modal, large-scale data flywheel
- Experience with multiple modalities (e.g., cameras, LiDAR, Radar).
- Experience with onboard edge deployment, cloud inference architectures, and balancing compute/efficiency trade-offs
What the JD emphasized
- AV auto-labeling system at scale
- auto-labeling models for Autonomous Vehicles at scale
- lidar-free auto-labeling
- multi-modal, large-scale data flywheel
Other signals
- delivering high-quality, scalable auto-labeling models
- ship production-grade models
- end-to-end ML lifecycle & data flywheel
- lidar-free auto-labeling
- Establish rigorous evaluation and monitoring benchmarks