AI Research Scientist Intern

AI Research Scientist Intern, Physical AI

Location: Woburn, MA (On-Site, 5 Days/Week)

Department: Physical AI, AI and Data Innovation

Duration: Summer 2026 3–6 Months (Preferred: 6 Months)

Our Opportunity:

The Chewy Robotics team is inviting applications for an AI Research Scientist Intern to conduct research that advances the field of Physical AI, the science of intelligence embodied in physical systems! This is an outstanding opportunity for PhD candidates to work alongside researchers at Chewy. They will develop and experimentally validate new methods in humanoid robotics, control theory, multi-agent fleet orchestration, and learning-based planning.

You will develop, implement, and analyze experiments on advanced robotic platforms to explore questions central to embodied intelligence: How do we teach robots to coordinate, manipulate, and act with human-like efficiency? Projects may span optimization-based task allocation, multi-agent path finding, motion learning, or tactile-augmented policy design for humanoids. Your findings are expected to result in publishable contributions in peer-reviewed conferences or journals.

Research Assistants operate as full members of our research team, collaborating with mechanical, electrical, and machine learning engineers and research scientists to bridge simulation and reality in humanoid control and perception systems. Work is conducted from our state-of-the-art Robotics Lab in Woburn, MA, where Physical AI models are trained and evaluated on real humanoid systems.

What You’ll Do:

• Conduct experimental or theoretical research in humanoid control and estimation, optimization, or Physical AI under the direction of Chewy Research Scientists.
• Build and implement algorithms for learning-based control, task assignment, estimation, or motion planning in multi-agent robotic systems.
• Integrate and validate algorithms on physical robotic platforms and analyze system behavior through data-driven methods.
• Develop experimental protocols and datasets for publication, with an emphasis on reproducibility and rigor.
• Collaborate cross-functionally with Chewy’s AI, robotics, and systems engineering teams to accelerate the embodiment of intelligence in physical agents.
• Prepare manuscripts, technical reports, and internal presentations that detail research progress.  

What You’ll Need:

• Currently enrolled in PhD program studying Robotics, Electrical Engineering, Mechanical Engineering, Computer Science, Applied Mathematics, or related field, with a focus on controls, optimization, robotics, or AI. 
• Proficiency with any combination of Python, PyTorch, and ROS2.
• Solid understanding of control theory, estimation theory, optimization, or machine learning for physical systems (e.g., reinforcement learning, imitation learning, model-based control and estimation).
• Exposure to one or more multi-agent systems, task allocation, multi-agent motion planning, collaborative estimation.
• Strong experimental or analytical reasoning skills with demonstrated ability to develop and validate algorithms through data.
• Proven publication record in top-tier venues (e.g., NeurIPS, ICLR, ICML, ICRA, IROS, CDC).
• Excellent written communication skills and motivation to publish scientific results at top-tier conferences and journals.

Bonus:

• Experience in visual servo control, system identification, or learning-based control.
• Experience working with robot simulation tools (e.g., MuJoCo, Isaac Sim, PyBullet) and physical systems integration.  
• Familiarity with robot perception (vision, tactile, or multimodal sensing).
• Familiarity with modern optimization modeling tools such as CasADi and OR-Tools and related optimization problems (e.g., LP, QP, SOCP, MIP, NLP)
• Prior experience with hardware-in-the-loop or real-robot experimentation.