Software Engineer, Analytics Platform at zoox

Zoox is transforming transportation with a mission to build autonomous robotaxis from the ground up—delivering a safer, cleaner, more reliable, and enjoyable future for all.   The Robot Data Platform team, as part of the Data Infrastructure organization, builds and operates the systems that manage data generated by Zoox’s autonomous vehicle fleet. We design, scale, and maintain infrastructure that ingests, stores, and serves hundreds of petabytes of vehicle data, empowering machine learning, data science, software engineering, fleet management, and safety analysis across the company.   As a member of this team, you’ll work alongside world-class software engineers to solve complex challenges in large-scale distributed data platforms, systems, and pipelines, shaping how Zoox leverages data to advance autonomous transportation. In this role, you will: • Develop features for the schedule-based processing framework built on top of Airflow, AWS EMR, and DuckDB. • Improve the stability, performance, and scalability of our data ingestion and processing platforms as we scale our geofence and robotoxi deployment. • Collaborate with cross-functional teams, such as software engineers, data scientists, data engineers, and TPMs, to gather requirements, design robust architectures, and implement effective solutions. • Partner with Staff and Senior engineers inside and outside the organization to translate user pain points into concrete technical solutions and roadmap items. • Enhance system observability by building monitoring and alerting tools to track performance and measure success. Qualifications • Bachelor's or Master's in Computer Science or related fields with 4+ years of industry experience in software engineering • Strong background in Python for large-scale data processing • Familiarity with large-scale data processing systems like Spark, Trino, and DuckDB • Experience in using cloud services, such as AWS, GCP, or Azure • Strong experience in troubleshooting data pipeline failures and optimizing pipeline performance and cost efficiency