Full-stack Software Engineer, Zoox

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• 5+ years of experience in software engineering and front-end application development
• Experience designing and building modern data-intensive applications such as dashboards for data warehouses that query and visualize data at a large scale
• Experience with modern web frameworks (e.g. react, angular, vue), python API frameworks (e.g. FastAPI, Django), and writing high-quality backend Python code
• Knowledge of SQL basics

Desirable

• We are seeking a highly skilled software engineer to design and develop tools that support and facilitate AI model development for the Perception component of our autonomous driving stack
• In this role, you will work closely with Machine Learning engineers, data scientists, and software engineers to build scalable and high-performance applications that enable ML engineers to efficiently curate and build model training datasets, as well as evaluate their models across petabytes of multimodal data
• You will be responsible for conducting the design and implementation of various MLOps applications and data processing systems, ensuring that they follow best practices to maximize the tooling efficiency and ML engineers’ productivity
• Collaborate with ML engineers and data scientists to understand business requirements and design MLOps applications that facilitate effective data operations, reveal data insights, and boost engineers’ productivity in ML model training and validation
• Develop the front end of the MLOps applications, for example, a Dataset Management platform that enables ML engineers to curate label data for model training. Another example is the Metrics platform that enables ML engineers to build their own metrics and dashboards to evaluate the performance of their models
• Collaborate with backend engineers and data engineers to integrate seamlessly with data pipelines and workflows on top of modern data processing technologies such as Apache Spark and Airflow, to support the real-time processing and visualization of large-scale data
• Monitor and optimize the performance of the tooling and applications to ensure high availability, scalability, and reliability, to support the development of mission-critical autonomous driving technology
• Continuously define problems and opportunities to iterate the application with a visionary product roadmap that keeps stakeholders on the same page
• Stay up-to-date with the latest trends and technologies in MLOps and data visualization, specifically related to autonomous driving perception and machine learning