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Sr. Autonomy Data Collection and Prototyping Engineer

Job in Palo Alto, Santa Clara County, California, 94306, USA
Listing for: Rivian
Full Time position
Listed on 2026-01-16
Job specializations:
  • Engineering
    Data Engineer, Software Engineer
Job Description & How to Apply Below

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

As part of the Autonomy Data Collection and Prototyping team, you will be fundamental in ensuring the quality, integrity, and analytical value of the data generated by Rivian’s ADAS/Autonomy test platforms. You will lead the strategy, implementation, and optimization of the end‑to‑end data pipeline, from vehicle logging through cloud processing and final analysis. This role is responsible for establishing and maintaining key performance indicators (KPIs) and data quality standards that directly influence the successful development and deployment of our features.

Furthermore, you will be a key contributor to developing on‑vehicle Engineering and Operations Tooling HMI (Human‑Machine Interface) necessary for efficient test fleet management and data acquisition. This role works cross‑functionally with SW development, Data Science, and Cloud Engineering teams based locally and throughout Rivian locations around the country. This role is expected to be onsite, with occasional travel required.

Responsibilities
  • Establish and Lead Data Strategy:
    Define, monitor, and report on Key Performance Indicators (KPIs) for data collection fleet health, data quality, cloud processing efficiency, and Hardware‑in‑the‑Loop (HiL) testing metrics.
  • Drive Data Quality & MLOps for Rapid Iteration:
    Architect, maintain, and implement automated tooling to ensure the integrity and completeness of logged data, focusing heavily on data quality for annotation and providing optimized feedback loops to drive rapid model enhancement and iteration. This includes monitoring for data drift and schema changes.
  • Oversee Cloud Processing Pipeline:
    Optimize and manage scalable cloud‑based processing pipelines (e.g., using Spark/PySpark) for raw and transformed ADAS/Autonomy data, including feature engineering pipelines.
  • Engineering Tooling HMI Development:
    Design and develop the on‑vehicle user interface code that allows test teams to manage data logging, view system status, and validate systems in real‑time, integrating with necessary vehicle services.
  • Hardware & System Debugging:
    Support advanced triage of system issues using deep technical knowledge of Linux tooling, vehicle network (Ethernet), and sensor interfaces to rapidly identify hardware and low‑level software bugs.
Qualifications
  • A bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or a closely related field is required.
  • 2+ years of experience in software development, data engineering, or MLOps, with a focus on data pipelines and high‑volume data systems.
  • Data Pipeline & MLOps Proficiency:
    Proven experience defining data quality standards and expertise with distributed processing frameworks (Spark/PySpark, Dask) and/or feature stores for managing ML data.
  • Cloud Proficiency:
    Strong experience with cloud computing platforms (e.g., AWS, GCP) and services relevant to large‑scale data processing.
  • HMI/UI Development:
    Experience with mobile, embedded, or desktop UI/UX development frameworks (e.g., Java/Kotlin/Swift, React/Angular, or other HMI tools) to build functional engineering tools.
  • System‑Level Debugging:
    • Expertise in Linux command‑line tooling, bash scripting, and system‑level diagnostics.
    • Strong knowledge of network architecture (TCP/IP, UDP, PTP) and debugging vehicle Ethernet and high‑speed data links.
    • Deep understanding of ADAS Sensors (Lidar, Radar, Camera) and their application for data collection and debugging.
  • Proficiency in Python (for data processing/analysis) and professional experience with C++ for developing high‑performance components.
Pay Disclosure

Salary Range for California Based Applicants:…

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