Staff Autonomy Engineer

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. 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. Position Overview: As a Staff Autonomy Engineer on our R&D team, you'll be one of the technical leaders setting direction for the AI that powers our robots. This is a hands-on technical leadership role: you'll architect systems that span perception, mapping, prediction, and planning; drive multi-quarter initiatives from research idea to fleet rollout; and raise the technical bar across the org. You'll work across team boundaries, partnering with engineering managers, product, and hardware to make decisions that ripple through the roadmap. You'll also be someone the team learns from — through code review, design review, mentorship, and the standards you set in your own work. We expect Staff engineers to be comfortable in deep technical waters across the modern autonomy stack: foundation models, learned policies, classical estimation and planning, and the production systems that knit them together. Essential Job Functions: Technical Leadership and Direction Set technical direction for major areas of the autonomy stack — perception, SLAM, prediction, planning, or the ML systems underneath them — and own the multi-quarter roadmaps that follow. Lead large, ambiguous projects end-to-end: scope the problem, pick the approach, drive execution across multiple engineers, and ship to the fleet. Make and defend the build-vs-borrow, learn-vs-classical, and depth-vs-breadth decisions that shape what the team works on for the next year. Partner with engineering managers, product, and hardware on staffing, sequencing, and risk; serve as a senior technical voice in cross-functional planning. Represent the team externally where useful — publications, conferences, recruiting, customer technical conversations. Technical Excellence Architect and contribute to systems spanning learned perception (transformer-based detectors, VLMs for scene understanding), neural and classical SLAM (including Gaussian splatting and implicit representations where they earn their keep), behavior prediction, and motion planning. Drive adoption of modern learning approaches — imitation learning, diffusion policies, vision-language-action models, RL where appropriate — and integrate them with the classical components that still do real work in production. Build and improve the data engines and evaluation infrastructure that turn fleet logs into training data, regression suites, and shipped wins. Lead performance and deployment work on embedded compute: model optimization, quantization, distillation, and runtime engineering on GPU/accelerator hardware. Stay current with the literature and translate the parts that matter into our codebase — and recognize the larger fraction that doesn't. Mentorship, Hiring, and Culture Mentor Senior and mid-level engineers; raise the bar through design review, code review, and the technical standards you model. Play a leading role in hiring: shape interview loops, calibrate decisions, close strong candidates, and own onboarding for new hires on the team. Champion engineering practices that compound — testing, observability, documentation, simulation infrastructure, developer tooling — and invest in the parts of the org that enable everyone else to ship faster. Education and/or Work Experience Requirements: Master’s Degree. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related field — or equivalent demonstrated experience. 7+ years of industry experience in embodied AI (robotics, self-driving, drones, or similar), with at least some of that time in a senior or lead capacity. Deep expertise in two or more of: machine learning, SLAM and state estimation, robotic perception, motion planning. Strong fluency in C++ and Python in a Linux environment. Demonstrated history of taking research ideas into production at meaningful scale. Required Knowledge, Skills, Abilities, and Other Characteristics: Deep, hands-on expertise with PyTorch (and/or JAX), modern training infrastructure, and contemporary architectures (transformers, diffusion models, VLA/foundation models). Extensive experience designing robotic systems with ROS 2 (or comparable middleware) and modern simulation environments such as Isaac Sim, MuJoCo, or Gazebo. Strong record of shipping ML-driven features to production hardware — including the post-launch reality of monitoring, debugging weird failure modes, rollback, and on-call. Excellent systems and architectural judgment; able to defend a position and equally able to update it when the evidence changes. Modern software engineering fundamentals: CI/CD, code review culture, observability, and iterative delivery. Excellent written and verbal communication — design docs that set direction, talks that bring people along, and code that other engineers want to read. Bonus: experience with on-device acceleration