Senior Perception Engineer, Chef Robotics

• BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a closely related field
• 5+ years of combined research and industry experience in computer vision and machine learning, with a track record of shipping perception systems to production
• Deep expertise in at least two of: instance/semantic segmentation, object detection, 3D perception, or multi-object tracking
• Strong Python skills; experience building production-quality, maintainable code — not just research prototypes
• Hands-on experience with deep learning frameworks (PyTorch strongly preferred) and the full training pipeline from data to deployed model
• Experience working with RGBD sensors, depth cameras, and point cloud data
• Proven ability to build and optimize models for low-latency, real-time inference
• Familiarity with ROS or similar robotics middleware

Desirable

• Chef Robotics is building autonomous robots that work alongside humans in commercial food preparation environments — and perception is at the heart of what makes them reliable
• As a Perception Engineer, you will own the full stack of how our robots see and understand the world: from integrating cutting-edge camera hardware, to training production-grade deep learning models, to ensuring those models perform accurately and efficiently in real-time on the factory floor
• You will work on some of the most technically rich problems in applied robotics — dense instance segmentation of deformable food items, real-time inference under tight latency constraints, sensor fusion, and robust tracking in cluttered, dynamic environments
• You will not just train models; you will design the pipelines that gather and curate data, define the architectures that balance accuracy and speed, and own the deployment and field troubleshooting of what you build
• We are a small, high-ownership team. We work onsite five days a week and move with startup urgency — you will be expected to go deep technically while staying pragmatic about what ships
• Design, train, and optimize deep learning models for detection, segmentation, segmentation, pose estimation, and classification — with a focus on real-world robustness over benchmark performance
• Build low-latency inference pipelines that approach real-time performance; profile and optimize models for deployment on embedded and edge hardware
• Develop and improve multi-object tracking algorithms for reliable identification and motion prediction of items across frames
• Solve challenging perception problems specific to food robotics: deformable objects, occlusions, varying lighting, and high visual similarity between categories
• Own the end-to-end ML lifecycle: data collection strategy, annotation tooling, dataset curation, augmentation pipelines, model training, evaluation, deployment, and field debugging
• Develop tooling to monitor model performance in production and drive continuous improvement cycles
• Partner closely with robotics, hardware, and software engineers to translate perception capabilities into reliable end-to-end robot behaviors
• Help define the perception roadmap and influence technical direction as the team grows
• Assist in integrating new cameras and sensors for enhanced robotic vision