Machine Learning / Computer Vision Engineer

Full Time

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Cambridge, MA

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Eka Robotics

Eka Robotics is on a mission to build intelligence for the physical world by creating robots that are fast, general-purpose, and reliable. Our physics-based approach enables robots to achieve superhuman capabilities and pushes the frontier of robotics research and deployment.

Our team is composed of pioneers in robotics and machine learning. As we continue to scale our research and development efforts, we're looking for hands-on engineers who are excited to help shape the future of robotics.

Responsibilities

  • Build computer vision and visual representation learning pipelines for robotic manipulation using:

    • RGB and RGB-D imagery

    • Depth sensing

    • Semantic segmentation

    • Pose estimation

    • Keypoint detection

    • Object-centric representations

  • Develop visual models that support reinforcement learning and imitation learning, including end-to-end visuomotor policies that map visual observations directly to robot actions.

  • Improve vision data pipelines through:

    • Domain randomization

    • Photorealistic rendering

    • Synthetic data generation

    • Sensor noise modeling

    • Real-world fine-tuning

  • Design and train perception models that are robust to:

    • Lighting variations

    • Camera viewpoint changes

    • Texture variation

    • Clutter and occlusion

    • Object instance diversity

    • Imperfect sensor calibration

  • Evaluate learned visual representations and manipulation policies on real robotic systems, identify failure modes, and iterate on models, datasets, and training strategies.

  • Collaborate closely with robotics, robot learning, and simulation engineers to define perception strategies for robotic manipulation.

  • Configure, calibrate, and evaluate camera and depth sensing systems, with an emphasis on how sensor selection impacts learned policies and real-world robustness.

Minimum Qualifications

  • Ph.D. in Computer Vision, or 3+ years of experience developing production computer vision systems.

  • Strong background in machine learning for computer vision, particularly deep learning-based visual perception.

  • Experience training modern computer vision models using frameworks such as:

    • JAX

    • PyTorch

    • or equivalent frameworks

  • Practical experience with one or more of the following:

    • Visual representation learning

    • Object detection

    • Semantic segmentation

    • Pose estimation

    • Depth estimation

    • Object tracking

    • 3D perception

  • Strong Python programming skills.

  • Ability to transition seamlessly between research prototypes and production-quality software.

  • Strong understanding of how factors such as data distribution, sensor noise, calibration, lighting, and scene variation influence model performance.

Preferred Qualifications

Experience with one or more of the following:

  • Training robot policies from visual observations, including:

    • RGB

    • RGB-D

    • Point clouds

    • Object-centric representations

    • Learned latent representations

  • Domain randomization, synthetic data generation, differentiable rendering, neural rendering, or photorealistic simulation.

  • Robotics simulation platforms such as:

    • Isaac Sim

    • MuJoCo

    • or similar environments

  • Robot learning techniques including:

    • Reinforcement learning

    • Behavior cloning

    • Diffusion policies

    • Offline reinforcement learning

    • Learning from demonstrations

  • Deploying perception systems on real robots, including:

    • Camera calibration

    • Hand-eye calibration

    • Depth sensors

    • ROS or ROS2

    • Robot data collection pipelines

  • First-author publications at leading computer vision, robotics, or machine learning conferences, including:

    • CVPR

    • ICCV

    • ECCV

    • NeurIPS

    • ICLR

    • ICML

    • RSS

    • CoRL

    • ICRA

    • IROS