Internship Openings

5 / 18 Intern positions were found.

Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL's employees to perform their job duties may result in discipline up to and including discharge.

Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.


  • CA0148: Internship - Motion Planning and Control for Autonomous Articulated Vehicles

    • MERL is seeking an outstanding intern to collaborate in the development of motion planning and control for autonomous articulated vehicles. The ideal candidate is expected to be working towards a PhD in electrical, mechanical, aerospace engineering, robotics, control or related areas, with a strong emphasis on motion planning and control, possibly with applications to ground vehicles, to have experience in at least some of path/motion planning algorithms (A*, D*, graph-search) and optimization-based control (e.g., model predictive control), to have excellent coding skills in MATLAB/Simulink and a strong publication record. The expected start date is the Spring/Early Summer 2025 and the expected duration is 6-9 months, depending on candidate availability and interests.

      Required Specific Experience

      • Path/motion planning algorithms (A*, D*, graph-search)
      • Nonlinear model predictive control
      • Programming in Matlab/Simulink
      • Applications to mobile robots or vehicles

      Additional Useful Experience

      • Nonlinear MPC Design in CasADi
      • Code generation tools and dSPACE
      • Applications to autonomous vehicles and articulated vehicles

    • Research Areas: Control, Dynamical Systems, Robotics
    • Host: Stefano Di Cairano
    • Apply Now
  • CV0063: Internship - Visual Simultaneous Localization and Mapping

    • MERL is looking for a self-motivated graduate student to work on Visual Simultaneous Localization and Mapping (V-SLAM). Based on the candidate’s interests, the intern can work on a variety of topics such as (but not limited to): camera pose estimation, feature detection and matching, visual-LiDAR data fusion, pose-graph optimization, loop closure detection, and image-based camera relocalization. The ideal candidate would be a PhD student with a strong background in 3D computer vision and good programming skills in C/C++ and/or Python. The candidate must have published at least one paper in a top-tier computer vision, machine learning, or robotics venue, such as CVPR, ECCV, ICCV, NeurIPS, ICRA, or IROS. The intern will collaborate with MERL researchers to derive and implement new algorithms for V-SLAM, conduct experiments, and report findings. A submission to a top-tier conference is expected. The duration of the internship and start date are flexible.

      Required Specific Experience

      • Experience with 3D Computer Vision and Simultaneous Localization & Mapping.

    • Research Areas: Computer Vision, Robotics, Control
    • Host: Pedro Miraldo
    • Apply Now
  • CV0075: Internship - Multimodal Embodied AI

    • MERL is looking for a self-motivated intern to work on problems at the intersection of multimodal large language models and embodied AI in dynamic indoor environments. The ideal candidate would be a PhD student with a strong background in machine learning and computer vision, as demonstrated by top-tier publications. The candidate must have prior experience in designing synthetic scenes (e.g., 3D games) using popular graphics software, embodied AI, large language models, reinforcement learning, and the use of simulators such as Habitat/SoundSpaces. Hands on experience in using animated 3D human shape models (e.g., SMPL and variants) is desired. The intern is expected to collaborate with researchers in computer vision at MERL to develop algorithms and prepare manuscripts for scientific publications.

      Required Specific Experience

      • Experience in designing 3D interactive scenes
      • Experience with vision based embodied AI using simulators (implementation on real robotic hardware would be a plus).
      • Experience training large language models on multimodal data
      • Experience with training reinforcement learning algorithms
      • Strong foundations in machine learning and programming
      • Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

    • Research Areas: Artificial Intelligence, Computer Vision, Speech & Audio, Robotics, Machine Learning
    • Host: Anoop Cherian
    • Apply Now
  • OR0115: Internship - Whole-body dexterous manipulation

    • MERL is looking for a highly motivated individual to work on whole-body dexterous manipulation. The research will develop robot motor skills for whole-body, dexterous manipulation using optimization and/or learning algorithms. The ideal candidate should have experience in either one or multiple of the following topics: Optimization Algorithms for contact systems, Reinforcement Learning, control through contacts, and Behavioral cloning. Senior PhD students in robotics and engineering with a focus on contact-rich manipulation are encouraged to apply. Prior experience working with physical robotic systems (and vision and tactile sensors) is required as results need to be implemented on a physical hardware. Good coding skills in Python ML libraries like PyTorch etc. and/or relevant Optimization packages is required. A successful internship will result in submission of results to a peer-reviewed robotics journal in collaboration with MERL researchers. The expected duration of internship is 4-5 months with start date in May/June 2025. This internship is preferred to be onsite at MERL.

      Required Specific Experience

      • Prior experience working with physical hardware system is required.
      • Prior publication experience in robotics venues like ICRA,RSS, CoRL.

    • Research Areas: Robotics, Optimization, Artificial Intelligence, Machine Learning
    • Host: Devesh Jha
    • Apply Now
  • OR0127: Internship - Deep Learning for Robotic Manipulation

    • MERL is looking for a highly motivated and qualified intern to work on deep learning methods for detection and pose estimation of objects using vision and tactile sensing, in manufacturing and assembly environments. This role involves developing, fine-tuning and deploying models on existing hardware. The method will be applied for robotic manipulation where the knowledge of accurate position and orientation of objects within the scene would allow the robot to interact with the objects. The ideal candidate would be a Ph.D. student familiar with the state-of-the-art methods for pose estimation and tracking of objects. The successful candidate will work closely with MERL researchers to develop and implement novel algorithms, conduct experiments, and publish research findings at a top-tier conference. Start date and expected duration of the internship is flexible. Interested candidates are encouraged to apply with their updated CV and list of relevant publications.

      Required Specific Experience

      • Prior experience in Computer Vision and Robotic Manipulation.
      • Experience with ROS and deep learning frameworks such as PyTorch are essential.
      • Strong programming skills in Python.
      • Experience with simulation tools, such as PyBullet, Issac Lab, or MuJoCo.

    • Research Areas: Computer Vision, Robotics, Artificial Intelligence
    • Host: Siddarth Jain
    • Apply Now