Staff ML Engineer, Perception: Auto-labeling

About Us

Rivianis 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.

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.

• 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.

• 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 o 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

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

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,…