Senior Software Engineer - Performance Prediction

<p><span style="font-weight: 300; font-family: arial, helvetica, sans-serif;"><span style="font-weight: 400;">Latitude AI (</span><a href="https://lat.ai/"><span style="font-weight: 400;">lat.ai</span></a><span style="font-weight: 400;">) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone.</span></span></p> <p><span style="font-family: arial, helvetica, sans-serif;"><span style="font-weight: 300;">When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – </span><strong><em>all dedicated to making a real, positive impact on the driving experience for millions of people.&nbsp;</em></strong></span></p> <p><span style="font-weight: 300; font-family: arial, helvetica, sans-serif;">As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.</span></p> <p><span style="font-family: arial, helvetica, sans-serif;"><strong>Meet the team:</strong></span></p> <p>The Performance Prediction team builds the Machine Learning models, evaluation pipelines, and internal tools that help us understand how autonomy behavior changes across software releases. We work on problems that span behavior classification, ride quality detection, probabilistic trajectory prediction, and release regression analysis.</p> <p>Our systems support both classical and modern ML approaches. That includes compact learned classifiers such as tree-based models for behavior and ride quality detection, as well as deep learning-based probabilistic prediction models for more complex autonomy tasks. We also build the software around those models: dataset definition, feature generation, training and tuning workflows, offline metrics, experiment tracking, and tools that help engineers inspect regressions at the slice and scenario level.</p> <p>This role is a strong fit for someone who enjoys building reliable Python systems and applying rigorous ML evaluation methods in a safety-critical domain. In practice, the work is a mix of ML systems development, model and evaluation work, and internal tooling used by partner teams across autonomy.</p> <p><span style="font-family: arial, helvetica, sans-serif;"><strong>What you’ll do:&nbsp;</strong><