Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.
The Collision Avoidance System (CAS) is responsible for detecting and reacting to imminent collision situations in support of our vehicle’s overall safety goals. CAS Perception is responsible for processing raw sensor data from our vehicle’s world-class sensor suite using a combination of geometric, interpretable algorithms and deep learning to detect near-collisions with obstacles along our intended driving path, in the most challenging dense urban environments and under tight compute resource constraints. Overall CAS is parallel and complementary to our Main AI autonomy stack, and has a close relationship with our vehicle hardware and safety teams in order to architect redundancy into our overall driving system.
As a Data Engineer on the Collision Avoidance system, you will play a crucial role in driving dat-driven decision making by ensuring the availability of high-quality, reliable data and metrics for the team and Zoox as a whole. By doing so, you will contribute significantly to making autonomous vehicles safer for all passengers.
Compensation
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. The salary will range from $164,000 - $265,000. A sign-on bonus may be part of a compensation package. Compensation will vary based on geographic location, job-related knowledge, skills, and experience.
Zoox also offers a comprehensive package of benefits including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
In this role, you will:
Design the data pipelines to support Zoox’s machine learning systems and data mining at scaleDevelop and maintain ETL (Extract, Transform, Load) processes for data ingestion and transformation to make data readily available for ML models training and validation.Maintain high quality data pipelines implementing data quality checks, ensuring data consistency and accuracy and adherence to data engineering best practices.Design and implement data models and data storage solutions for fast, reliable and user-friendly data querying.Build self-serve data dashboards for quick fact checking and ongoing reporting purposes.Collaborate with data scientists, software engineers, and other stakeholders to ensure that the data pipelines meet the requirements for machine learning models and make recommendations for changes or upgrades.Collaborate with legal, infrastructure, platform teams to develop effective solutions that aligns with data access, retention, privacy protection policies and regulations.Qualifications
BS/MS degree in a technical fieldExperience designing and building complex data infrastructure at scaleAdvanced Structure Query Language (SQL) and data warehousing experienceExperience operating a workflow manager such as AirflowExperience with large scale streaming platforms (e.g. Kafka, Kinesis), processing frameworks (e.g. Spark, Hadoop) and storage engines (e.g. HDFS, HBase)Bonus Qualifications
Exceptional Python or Scala skillsBasic fluency in C++Familiarity with or exposure to experimentation platformsA strong DataOps mindset and opinions on next-generation warehousing tools