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