Role SummaryAuto-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 DisclosureSalary 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.
