AI Research Scientist Intern
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Job Location:
Woburn, MA - USA
Monthly Salary:
Not Disclosed
Posted on:
12 hours ago
Vacancies:
1 Vacancy
Job Summary
AI Research Scientist Intern Physical AI
Location: Woburn MA (On-Site 5 Days/Week)
Department: Physical AI AI and Data Innovation
Duration: Summer 2026 36 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 Youll 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 Chewys 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 Youll 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.
Required Experience:
Intern
Key Skills
- Robotics
- Machine Learning
- Python
- AI
- C/C++
- Data Collection
- Research Experience
- Signal Processing
- Natural Language Processing
- Computer Vision
- Deep Learning
- Tensorflow
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