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 Mapping is a foundational pillar of the Autonomy stack. In this Tech Lead (TL) role, you will own, architect, drive and deliver the strategy to push the frontiers on mapping. The scope includes multi-vehicle mapping (with and without lidar), 3D/4D mapping without lidar, HD maps & localization (including places without GNSS), among other critical applications. You will ship production-grade pipelines that push the boundaries of what’s possible in mapping. As such, you will also own and drive the whole end-to-end ML lifecycle & data flywheel for these aforementioned mapping applications: data acquisition, metrics definition, evaluation, model performance optimization, feedback loop. You will also represent these mapping capabilities in our interactions with other teams. You will also work broadly with the rest of the Autonomy org to continuously improve and expand the capabilities of the mapping system as well as defining the requirements. Responsibilities Own, architect, drive and deliver the overarching strategy to push the frontiers on mapping. Scope especially includes multi-vehicle mapping (with and without lidar), 3D/4D mapping without lidar, HD maps & localization, among other critical applications. Ship a production-grade, high-quality, robust, scalable mapping pipeline. 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 mapping system and its capabilities. 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. Qualifications Education: BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a highly related quantitative field. Experience: 7+ years of professional experience building, scaling and shipping ML solutions, with a strong focus on the following: AV mapping at scale: Proven track record of hands-on experience delivering a mapping system for Autonomous Vehicles at scale. Architect/Leadership: Experience with defining, driving and delivering a mapping strategy for AV. Sensor modularity: Experience in shipping mapping systems with different sensor modalities in AV, especially both with and without lidar. Perception stack: holistic understanding of the entire AV perception stack. System engineering: Strong proficiency in Python and/or C++ 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. Preferred Qualifications Strong experience in AV mapping, especially from multiple vehicle passes and across time. Strong experience in Lidar-free mapping for Autonomous Vehicles Experience in building HD maps and SD maps. Experience in Localization given a map, with and without lidar. Experience in defining and driving a 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 auto-labeling (lane, objects, etc) Experience with onboard edge deployment, cloud inference architectures, and balancing compute/efficiency trade-offs. Experience with optimization, hyper-scalability, high-performance inference engines like TensorRT. 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.
Tech Lead, Perception Autonomy
Tech Lead for Perception Autonomy at Rivian, focusing on architecting and delivering production-grade ML pipelines for AV mapping, including multi-vehicle, lidar-free, and HD mapping/localization. Owns the end-to-end ML lifecycle from data acquisition to optimization and evaluation, with a strong emphasis on shipping scalable solutions.
What you'd actually do
- Own, architect, drive and deliver the overarching strategy to push the frontiers on mapping.
- Ship a production-grade, high-quality, robust, scalable mapping pipeline.
- 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 mapping system and its capabilities.
- 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 building, scaling and shipping ML solutions
- Proven track record of hands-on experience delivering a mapping system for Autonomous Vehicles at scale.
- Experience with defining, driving and delivering a mapping strategy for AV.
- Experience in shipping mapping systems with different sensor modalities in AV, especially both with and without lidar.
- Holistic understanding of the entire AV perception stack.
- Strong proficiency in Python and/or C++
- 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.
Nice to have
- Strong experience in AV mapping, especially from multiple vehicle passes and across time.
- Strong experience in Lidar-free mapping for Autonomous Vehicles
- Experience in building HD maps and SD maps.
- Experience in Localization given a map, with and without lidar.
- Experience in defining and driving a 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 auto-labeling (lane, objects, etc)
- Experience with onboard edge deployment, cloud inference architectures, and balancing compute/efficiency trade-offs.
- Experience with optimization, hyper-scalability, high-performance inference engines like TensorRT.
What the JD emphasized
- shipping production-grade pipelines
- push the frontiers on mapping
- multi-vehicle mapping (with and without lidar)
- 3D/4D mapping without lidar
- HD maps & localization
- end-to-end ML lifecycle & data flywheel
- metrics definition
- evaluation
- model performance optimization
- feedback loop
- AV mapping at scale
- delivering a mapping system for Autonomous Vehicles at scale
- defining, driving and delivering a mapping strategy for AV
- shipping mapping systems with different sensor modalities in AV, especially both with and without lidar
- holistic understanding of the entire AV perception stack
- benchmarking tools
- infrastructure
- drive progress across a complex system spanning multiple domains and components, across a distributed, cross-functional stack in a fast-paced environment
- Lidar-free mapping for Autonomous Vehicles
- building HD maps and SD maps
- Localization given a map, with and without lidar
- defining and driving a training data strategy
- auto-labeling
- onboard edge deployment
- cloud inference architectures
- balancing compute/efficiency trade-offs
- optimization
- hyper-scalability
- high-performance inference engines like TensorRT
Other signals
- shipping production-grade ML pipelines
- end-to-end ML lifecycle & data flywheel
- multi-vehicle mapping
- 3D/4D mapping without lidar
- HD maps & localization