Machine Learning / Computer Vision Engineer

Roboticsconnected; Boston (MA); Presencial; USD 130.000 - 190.000; Tempo integral

Resumo da vaga

Eka Robotics in Boston is hiring a researcher to advance computer vision for robotic manipulation. You will build vision pipelines, train perception models, and collaborate with robotics engineers to deploy robust perception in real robots.

The role requires a Ph.D. in computer vision or 3+ years in CV, deep learning expertise, and strong Python skills. Experience with PyTorch/Jax and robotics simulators is a plus. This is a hands-on position with real-world impact.

Qualificações

• Ph.D. in computer vision or 3+ years of CV experience.
• Strong background in machine learning for computer vision, especially deep learning-based perception.
• Experience training modern CV models in PyTorch or Jax.
• Practical experience with visual representation learning, segmentation, depth estimation, or 3D perception.

Responsabilidades

• Build computer vision and visual representation learning pipelines for robotic manipulation.
• Develop visual models that support reinforcement and imitation learning policies.
• Improve data pipelines for vision-based manipulation, including domain randomization and synthetic data generation.
• Design and train perception models robust to lighting changes, viewpoint shifts, and occlusion.
• Evaluate learned representations on real robotic tasks and iterate on models and data.
• Collaborate with robotics and simulation engineers to define perception strategy.

Conhecimentos

PhD in computer vision or CV field

Deep learning for CV

Python programming

Research to production transition

Experience with PyTorch/Jax

Data distribution & calibration

Formação

PhD in computer vision

Ferramentas

PyTorch

Jax

ROS/ROS2

MuJoCo

Isaac Sim

Descrição da vaga

Eka Robotics

Eka Robotics is on a mission to build intelligence for the physical world - robots that are fast, general, and reliable. Our approach, grounded in physics, unlocks superhuman capabilities. We are defining the frontier of robotics research and deployment.

Our team consists of pioneers in robotics and machine learning. We are now hiring to scale our R&D effort. We are looking for hands-on individuals who are excited to help shape the future of robotics.

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 that support reinforcement learning and imitation learning policies, including end-to-end visuomotor policies that map visual observations to robot actions.
• Improve our data pipeline for vision-based manipulation policies through domain randomization, photorealistic rendering, synthetic data generation, sensor noise modeling, and real-world fine-tuning.
• Design and train perception models that are robust to lighting changes, camera viewpoint shifts, texture variation, clutter, occlusion, object instance variation, and imperfect calibration.
• Evaluate learned 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 the perception strategy for robotic manipulation.
• Set up, calibrate, and evaluate camera and depth sensing systems when needed, with an emphasis on how sensor choices affect learned policies and real-world robustness.

Minimum Qualifications

• Ph.D. in computer vision or 3+ years of experience working on a computer vision product.
• Strong background in machine learning for computer vision, especially 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 fluidly between research code and production-quality systems.
• Strong understanding of how data distribution, sensor noise, calibration, lighting, and scene variation affect model performance.

Preferred Qualifications

• Experience training policies from visual observations, including RGB, RGB-D, point clouds, object-centric representations, or learned latent representations.
• Experience with domain randomization, synthetic data generation, differentiable rendering, neural rendering, or photorealistic simulation.
• Experience with robotics simulators or synthetic data tools such as Isaac Sim, MuJoCo or similar environments.
• Familiarity with robot learning methods such as reinforcement learning, behavior cloning, diffusion policies, offline RL, or learning from demonstrations.
• Experience with real robot deployment, including camera calibration, hand-eye calibration, depth sensors, ROS/ROS2, or robot data collection pipelines.
• First-author publications in top computer vision, robotics, or machine learning venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, RSS, CoRL, ICRA, or IROS.