MERL is the US research arm of Mitsubishi Electric's corporate R&D group, based in Cambridge, MA. Its stack is simulation-first: Isaac Sim, MuJoCo, PyBullet and Gazebo sit alongside PyTorch, JAX, ROS 2 and OpenVLA, pointing to heavy investment in embodied AI and sim-to-real robotics. Hiring is overwhelmingly research interns, and velocity is slowing, suggesting a seasonal, project-driven intake rather than headcount expansion.
Mitsubishi Electric Research Laboratories (MERL) conducts basic and applied research for its parent, Mitsubishi Electric Corporation, from its headquarters in Cambridge, Massachusetts. Founded in 1991, the lab employs 51–200 people. Research areas span physical modeling and simulation, optimization, control, signal processing, computer vision, speech and audio, robotics and machine learning. The lab publishes openly and collaborates with the broader research community while transferring results back to Mitsubishi Electric business units. Current hiring is concentrated in the United States and focused on research roles.
MERL works with PyTorch, JAX, TensorFlow, Python, C++ and Julia, plus robotics and simulation tools such as ROS 2, Isaac Sim, MuJoCo, Gazebo and PX4, and control tools like MATLAB, Simulink, CasADi and Gurobi.
Yes. Interns make up the bulk of MERL's open roles, mostly in research, with a smaller set of staff-level positions. All hiring is in the United States, centered on Cambridge, MA.
Job posts point to embodied AI for dexterous manipulation, vision-language-action models for robot control, humanoid systems for material handling, task and sequence planning, and sim-to-real deployment.
Other companies in the same industry, closest in size
Mitsubishi Electric Research Laboratories's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.
