Internship Openings

Join our cutting-edge research team to help advance the next generation of Embodied AI and Humanoid Robotics. As a research intern, you will develop AI technologies that enable humanoid robots to understand, reason, and interact with the physical world through complex manipulation, assembly, and tool-use tasks. This is a unique opportunity to contribute to impactful research with the goal of publishing at leading AI and robotics conferences.

What You'll Work On

Depending on your background and interests, projects may include:

• Embodied AI for dexterous manipulation, assembly, and tool use
• Vision-Language-Action (VLA) models and Foundation Models for robotic control
• World-Action Models (WAM) for long-horizon planning and decision making
• Learning from human demonstrations, teleoperation, and autonomous data collection
• Sim-to-real transfer, reinforcement learning, and real-world robot deployment

What We're Looking For

We are seeking highly motivated graduate students with:

• Strong research experience in robotics, embodied AI, machine learning, computer vision, or related fields
• Experience with deep learning frameworks such as PyTorch or JAX, and strong Python programming skills
• Familiarity with one or more of the following:
  • Vision-Language-Action (VLA) models
  • Foundation Models or multimodal AI
  • Reinforcement learning or imitation learning
  • Robot manipulation, motion planning, or control
  • Agentic AI systems for robotics

Preferred qualifications:

• Hands-on experience with humanoid or loco manipulators (e.g., Unitree G1)
• Experience with teleoperation systems (e.g., Pico, Sonic)
• Experience with robotics simulators (e.g., Isaac Sim, MuJoCo, Genesis)
• Familiarity with ROS/ROS 2 and real-world robot experimentation
• Familiarity with policy deployment on edge AI devices (e.g., Jetson GPUs)

Internship Details

• Duration: Approximately 4 months
• Start Date: Flexible
• Location: Cambridge, MA
• Objective: Conduct high-impact research leading to publications at premier AI and robotics conferences (e.g., CoRL, RSS, ICRA, IROS, NeurIPS, ICML)

If you are excited about building AI that enables robots to perform complex real-world tasks—including assembly, tool use, and dexterous manipulation—we encourage you to apply.

The pay range for this internship position will be 6-8K per month.

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.).

We are seeking graduate students interested in helping advance the understanding of applying sophisticated audio processing techniques (e.g., source separation, localization, anomalous sound detection) to moving sound sources (e.g., vehicles). The interns will collaborate with MERL researchers to derive and implement novel algorithms, record data, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work. The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, source separation, physics informed machine learning, outlier detection, and unsupervised learning.

The pay range for this internship position will be 6-8K per month.

We are looking for a graduate student interested in advancing the field of multimodal scene understanding, focusing on scene understanding using natural language for robot dialog and/or indoor monitoring with a large language model. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work. The ideal candidates are senior Ph.D. students with experience in deep learning for audio-visual, signal, and natural language processing. Good programming skills in Python and knowledge of deep learning frameworks such as PyTorch are essential. Multiple positions are available with a flexible start date (not just Spring/Summer but throughout 2026) and duration (typically 3-6 months).

Required Specific Experience

• Experience with ROS2, C/C++, Python, and deep learning frameworks such as PyTorch are essential.

The pay range for this internship position will be 6-8K per month.