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Software Engineer, ML Data Infrastructure
Summary by AIEngineer.careers
Software Engineer, ML Data Infrastructure is a ML Infrastructure Engineer role (full-time). with Nuro. in MOUNTAIN VIEW, US. Compensation shown: $160K–$241K. Imported listing (source: dsfor.com). Apply on the employer's site (dsfor.com).
Imported listing (source: dsfor.com) · Apply on dsfor.com
MOUNTAIN VIEW · US
$160K–$241K
Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from dsfor.com
Nuro is seeking a Software Engineer to build and maintain large-scale ML data infrastructure for autonomous driving systems. You will design batch and streaming data pipelines, storage systems, and dashboards that support training and evaluation data. This role collaborates closely with system engineers to validate autonomous driving technology.
Responsibilities
- Design and develop unified, introspectable, large-scale batch and streaming data pipelines for evaluation use cases.
- Create and implement a storage system for large volumes of evaluation and performance metrics.
- Construct intuitive dashboards and reports to present evaluation results and highlight ML component improvements and regressions.
- Develop and maintain continuous testing and monitoring systems for data integrity and pipeline resilience.
- Develop data mining tools with applied ML techniques for data discovery in Perception, Behavior, and Mapping.
- Develop data annotation tools for first-party and third-party labeling workforce.
- Scale data annotation labels with state-of-the-art ML techniques.
Requirements
- BS, MS, or Ph.D. in Computer Science, Electrical Engineering, or a closely related field.
Nuro
Mountain View · US · 1139+ employees
Nuro is a self-driving technology company building a universal autonomy platform for the mobility ecosystem. It combines AI with automotive-grade hardware and licenses the Nuro Driver technology for applications including robotaxis, commercial fleets, logistics, and personally owned vehicles.
