Senior ML Engineer, Computer Vision

• Master’s degree in Robotics, Machine Learning, Computer Vision, Computer Science or a closely related field.
• 4+ years of experience in software development with a focus on robotic manipulation or related areas.
• Strong development expertise in Python and C++.
• Experience with major deep learning frameworks such as PyTorch.
• Experience with data science tools & libraries like numpy, pandas, scipy, matplotlib, scikit-learn
• Demonstrated experience training and adapting of existing machine learning architectures for computer vision, such as CNNs/ViTs or VLMs, to solve tasks such as grasp estimation, object detection/segmentation, depth estimation, or anomaly/outlier detection
• Demonstrated proficiency to solve real world computer vision problems with machine learning
• Demonstrated ability to: Develop on and troubleshoot real robotic systems Determine and communicate justification of technical priorities Rapidly prototype and iterate on solutions to challenging problems Work independently on a variety of projects while maintaining focus Work in a fast-paced environment with changing priorities Mentor junior engineers
• Develop on and troubleshoot real robotic systems
• Determine and communicate justification of technical priorities
• Rapidly prototype and iterate on solutions to challenging problems
• Work independently on a variety of projects while maintaining focus
• Work in a fast-paced environment with changing priorities
• Mentor junior engineers
• Strong verbal and written communication skills, capable of explaining complex ideas clearly and concisely to both technical and non-technical stakeholders
• Provide technical leadership on key projects

• MS / PhD in Machine Learning, Computer Vision, Computer Science or a closely related field.
• Demonstrated technical proficiency in computer vision applied to robotic manipulation, such as grasp estimation or long-tailed object detection & segmentation in clutter
• Experience with: Robotic vision sensors and camera to robot calibration Both RGB and depth data Collecting and training on real and synthetic datasets, including various forms of data augmentation Applying machine learning to hardware interacting with the real world Real-time perception-based control Robot simulators (e.g. Isaac Sim) Combining model-based and data-driven approaches Docker, cloud computing, or similar applications Experiment tracking and dataset management (e.g. Weights & Biases) Database systems as data source, such as MongoDB Parallel/distributed systems and asynchronous/concurrent programming ROS or ROS2
• Robotic vision sensors and camera to robot calibration
• Both RGB and depth data
• Collecting and training on real and synthetic datasets, including various forms of data augmentation
• Applying machine learning to hardware interacting with the real world
• Real-time perception-based control
• Robot simulators (e.g. Isaac Sim)
• Combining model-based and data-driven approaches
• Docker, cloud computing, or similar applications
• Experiment tracking and dataset management (e.g. Weights & Biases)
• Database systems as data source, such as MongoDB
• Parallel/distributed systems and asynchronous/concurrent programming
• ROS or ROS2

Possessing some of these preferred qualifications is great to have but not required and should not discourage applicants from applying.