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 Tech Lead Manager (TLM) role, you will own, architect, drive and deliver the overarching strategy for Lidar-free auto-labeling at large scale. This scope includes mapping, lanes auto-labelling, object auto-labelling, scaling 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 own the whole end-to-end ML lifecycle of this scope: data acquisition, metrics definition, evaluation, model performance optimization, feedback loop. You will also work broadly with the rest of the Autonomy org to continuously improve and expand the capabilities of the system as well as defining the requirements. As TLM, this role will include managing individual contributors (IC) supporting those activities. Responsibilities Own, architect, drive and deliver the overarching strategy for Lidar-free auto-labeling at large scale. This scope includes AV mapping, lanes auto-labelling, object auto-labelling and scaling. Ship production-grade, scalable auto-labeling models. Have a holistic understanding of the entire AV perception stack and work with the teams to define how we measure and monitor performance of the auto-labeling system. Work with the team to drive progress in improving the performance. 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 Define system requirements and guide cross-functional efforts through technical trade-off decisions. Manage individual contributors (IC) responsible for supporting the aforementioned responsibilities 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 delivering an auto-labeling system for Autonomous Vehicles at scale. Auto labeling for mapping, lanes auto-labelling and/or object auto-labelling. Auto-labeling architect: Experience with owning, driving and delivering an auto-labeling strategy for AV. Perception stack: holistic understanding of the entire 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, across a distributed, cross-functional stack in a fast-paced environment. Team management: experience managing software, robotics and/or machine learning teams. Preferred Qualifications Strong experience in Lidar-free lane auto-labeling Strong experience in mapping, especially from multiple vehicle passes Experience in driving training data strategy, including defining data annotation guidelines, partnering effectively with in-house and external 3P annotation vendors. Experience with multiple modalities (e.g., cameras, LiDAR, Radar). Experience with onboard edge deployment, cloud inference architectures, and balancing compute/efficiency trade-offs. Experience with quantization techniques (PTQ, QAT) and high-performance inference engines like TensorRT. Pay Disclosure The salary range for this role is $265,000-331,300 for San Francisco Bay Area based applicants. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, geographic location, shift, and organizational needs. We offer a comprehensive package of benefits for full-time and part-time employees, their spouse or domestic partner, and children up to age 26, including but not limited to paid vacation, paid sick leave, and a competitive portfolio of insurance benefits including life, medical, dental, vision, short-term disability insurance, and long-term disability insurance to eligible employees. You may also have the opportunity to participate in Rivian’s 401(k) Plan and Employee Stock Purchase Program if you meet certain eligibility requirements. Full-time employee coverage is effective on their first day of employment. Part-time employee coverage is effective the first of the month following 90 days of employment. More information about benefits is available at rivianbenefits.com. Equal Opportunity Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. 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Senior Tech Lead Manager, Perception
This role focuses on owning, architecting, and delivering the strategy for large-scale, Lidar-free auto-labeling for Autonomous Vehicles. It involves the end-to-end ML lifecycle, including data acquisition, metrics, evaluation, optimization, and feedback loops, with a focus on shipping production-grade models and managing individual contributors. The domain is robotics, specifically within the AV perception stack.
What you'd actually do
- Own, architect, drive and deliver the overarching strategy for Lidar-free auto-labeling at large scale. This scope includes AV mapping, lanes auto-labelling, object auto-labelling and scaling.
- Ship production-grade, scalable auto-labeling models.
- Have a holistic understanding of the entire AV perception stack and work with the teams to define how we measure and monitor performance of the auto-labeling system.
- Work with the team to drive progress in improving the performance.
- Establish rigorous evaluation and monitoring benchmarks.
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
- Proven track record of hands-on experience delivering an auto-labeling system for Autonomous Vehicles at scale.
- Experience with owning, driving and delivering an auto-labeling strategy for AV.
- Holistic understanding of the entire 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 system spanning multiple domains and components, across a distributed, cross-functional stack in a fast-paced environment.
- Experience managing software, robotics and/or machine learning teams.
Nice to have
- Strong experience in Lidar-free lane auto-labeling
- Strong experience in mapping, especially from multiple vehicle passes
- Experience in driving training data strategy, including defining data annotation guidelines, partnering effectively with in-house and external 3P annotation vendors.
- Experience with multiple modalities (e.g., cameras, LiDAR, Radar).
- Experience with onboard edge deployment, cloud inference architectures, and balancing compute/efficiency trade-offs.
- Experience with quantization techniques (PTQ, QAT) and high-performance inference engines like TensorRT.
What the JD emphasized
- Lidar-free auto-labeling at large scale
- ship production-grade models
- end-to-end ML lifecycle
- managing individual contributors (IC)
Other signals
- shipping production-grade models
- ML lifecycle ownership
- managing ML teams