What you'd actually do

Drive and deliver prod-grade, high-quality, scalable auto-labeling models.

Push the performance of lidar-free auto-labeling.

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

Skills

Required

BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a highly related quantitative field.
7+ years of professional experience scaling ML solutions
Python
modern Perception pipelines
benchmarking tools
infrastructure
complex system spanning multiple domains and components

Nice to have

Experience in Lidar-free auto-labeling
Experience in mapping, especially from multiple vehicle passes and/or lidar-free mapping.
Experience in defining data annotation guidelines and partnering effectively with in-house and external 3P annotation vendors.
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

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 Staff ML Engineer role, you will play a key role in driving and 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 drive 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 Drive and 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. Push the performance of lidar-free auto-labeling. 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 Collaborate across teams to define target requirements and guide technical trade-off decisions. Qualifications Education: BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a highly related quantitative field. Experience: 7+ years of professional experience scaling ML solutions, with a strong focus on the following AV auto-labeling system at scale: Proven track record of hands-on experience driving and 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 system spanning multiple domains and components, in a fast-paced environment. Preferred Qualifications Experience in Lidar-free auto-labeling Experience in mapping, especially from multiple vehicle passes and/or lidar-free mapping. Experience in defining data annotation guidelines and partnering effectively with in-house and external 3P annotation vendors. 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: $228,000 - $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 covers most of the premium 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. Candidate Data Privacy Rivian may collect, use and disclose your personal information or personal data (within the meaning of the applicable data protection laws) when you apply for employment and/or participate in our recruitment processes (“Candidate Personal Data”). This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security and preference information. 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.

Staff ML Engineer, Perception

What you'd actually do

Skills

Required

Nice to have

What the JD emphasized

Other signals