Senior Autonomy Engineer

<p>Brain Corp is a San Diego, California, USA-based AI company creating transformative core technology for the robotics industry. Our purpose is to create autonomous technology that helps the real world work better. Brain's robotic and AI solutions help retailers ensure that the right product is on the right shelf at the right price, in a clean environment. Through the BrainOS® Robotics Platform, which powers the largest global fleet of the Autonomous Mobile Robots (AMRs) in operation in commercial public spaces, Brain Corp delivers insightful and efficient automated solutions in both commercial floor cleaning and inventory management, empowering organizations and their employees to achieve more. Brain Corp currently powers more than 30,000 AMRs, representing the largest fleet of its kind in the world. Brain Corp is funded by the SoftBank Vision Fund, Clearbridge, and Qualcomm Ventures.</p> <p>Named a top workplace by the San Diego Union Tribune and USA today in 2025, we make life-changing impacts through innovation, helping workers globally unlock thier abilties in orchestration with intelligent machinges.</p> <p><strong>Position Overview:&nbsp;</strong></p> <p>As a Senior Autonomy Engineer on our R&amp;D team, you'll help define the next generation of software that lets robots perceive, learn, and act in unstructured indoor environments. You'll work across the modern autonomy stack — from learned perception and prediction to mapping and motion planning — and ship capabilities that generalize across our fleet. We're looking for someone equally comfortable reading a fresh arXiv paper, writing production C++/Python, and debugging behavior on a real robot in the lab. You'll help set technical direction, raise the bar on engineering quality, and mentor others on the team.</p> <p><strong>Essential Job Functions:</strong></p> <ul> <li>Design, train, and deploy machine learning systems for perception, SLAM, prediction, and motion planning that enable safe navigation around people and obstacles.</li> <li>Build and improve learned components — including transformer-based perception, vision-language models for scene understanding, diffusion or imitation-learning policies, and neural/Gaussian-splatting approaches to mapping — and integrate them with classical estimation and planning where it makes sense.</li> <li>Translate state-of-the-art research (papers, open-source releases, conference talks) into production-quality implementations on the robot.</li> <li>Develop data engines and evaluation infrastructure that turn fleet logs into training data, regression tests, and shipped improvements.</li> <li>Own features end-to-end: from prototype, through sim and on-robot validation, to fleet rollout, with measurement of real-world impact.</li> <li>Improve runtime performance of per