over 1 year ago
Boston, MA, USAMid Level
Responsibilities
- Build computer vision and visual representation learning pipelines for robotic manipulation, including RGB, RGB-D, depth, segmentation, pose, keypoint, and object-centric representations.
- Develop visual models and end-to-end visuomotor policies for reinforcement learning and imitation learning.
- Improve vision-based manipulation data pipelines using domain randomization, photorealistic rendering, synthetic data generation, sensor noise modeling, and real-world fine-tuning.
- Design and train perception models robust to lighting changes, viewpoint shifts, texture variation, clutter, occlusion, object variation, and imperfect calibration.
- Evaluate visual representations and policies on real robotic manipulation tasks, identify failure modes, and iterate on models, data, and training procedures.
- Collaborate with robotics, robot learning, and simulation engineers to define perception strategy for robotic manipulation.
- Set up, calibrate, and evaluate camera and depth-sensing systems and assess their impact on learned policies and real-world robustness.
Requirements
- Ph.D. in computer vision or at least 3 years of experience working on a computer vision product.
- Strong background in machine learning for computer vision, particularly deep learning-based visual perception.
- Experience training modern computer vision models in JAX, PyTorch, or similar frameworks.
- Practical experience with visual representation learning, object detection, segmentation, pose estimation, depth estimation, tracking, or 3D perception.
- Strong Python programming skills.
- Ability to move between research code and production-quality systems.
- Strong understanding of how data distribution, sensor noise, calibration, lighting, and scene variation affect model performance.
- Preferred experience with visual-observation policies, RGB-D data, point clouds, object-centric or learned latent representations, domain randomization, synthetic data generation, differentiable or neural rendering, and photorealistic simulation.
- Preferred experience with Isaac Sim, MuJoCo, or similar robotics simulators and synthetic data tools.
- Preferred familiarity with reinforcement learning, behavior cloning, diffusion policies, offline reinforcement learning, or learning from demonstrations.
- Preferred experience with real-robot deployment, camera and hand-eye calibration, depth sensors, ROS/ROS2, and robot data collection pipelines.
- Preferred first-author publications in venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, RSS, CoRL, ICRA, or IROS.
Categories
ML EngineeringRobotics
