<p>As a Software Engineer within the Autopilot AI Infrastructure team, you will work on reinforcing, optimizing, and scaling our infrastructure components supporting AI research activities for Autopilot and the Optimus.</p> <p>At the core of our autonomy capabilities are neural networks that the research team is designing to train on very large amounts of data, across large-scale GPU clusters and our supercomputer Dojo. Robustly training these models at scale and in the shortest amount of time is critical to our mission.</p> <p>We are building out the Machine Learning Platform that our engineers and leadership use to schedule, manage and monitor machine learning experiments, data pipelines and artifacts.</p> <ul> <li>Architect and implement scalable, user-friendly tools for AI workflows to track and visualize the lifecycle of machine learning experiments and models </li><li>Build robust tools and infrastructure to improve the machine learning team's velocity. This includes training and evaluation code in Python to back-end and front-end work in JavaScript </li><li>Collaborate closely with ML engineers to ensure tools are aligned with research needs </li><li>Design dashboards to provide real-time insights into performance and progress for our ML engineers and leadership </li><li>Coordinate required hardware resources with the team managing the cluster hardware to maintain high availability </li><li>Strong knowledge of Python, React, and Linux </li><li>Experience working with backend infrastructure components (relational databases, in-memory caches, message brokers) </li><li>Experience building modern web applications using Flask/Django and React/Redux or similar component-based libraries </li><li>Experience deploying services on Kubernetes and setting up CI/CD flows </li><li>Solid understanding of security principles and best practices </li><li>UI and graphic design sensibilities </li><li>Experience working with HPC clusters </li><li>Knowledge of machine learning, computer vision, or neural networks </li></ul>