Senior Perception Engineer

In this role, you will:

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.

What You Bring:

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.

Nice-to-have:

Experience using simulation environments (e.g. Isaac Sim, Gazebo) for synthetic data generation, domain randomization, and sim-to-real transfer of perception models.
C++ proficiency for performance-critical modules and embedded deployment.
Experience with cloud ML infrastructure (GCP, AWS) and containerization (Docker, Kubernetes).
Background in autonomous vehicles, warehouse robotics, or other perception-heavy robotics applications.
Contributions to open-source CV/ML projects or publications in top-tier venues (CVPR, ECCV, NeurIPS, etc.).

Chef Robotics is solving one of the hardest problems in AI: bringing intelligence into the physical world.

Our robots are already operating in production facilities every day, generating the real-world data that powers the next generation of embodied AI. If you want to build technology that leaves the lab, ships to customers, and transforms an industry, Chef is the place to do it.