AI Intern – Robotics AI

Job Description

AI Intern, Robotics | ndimensions labs

The Tone:
This is a Fall full-time internship at Ndimensions Labs, located in Boston, MA. Ndimensions Labs is a group of technologists focused on building the infrastructure and learning systems behind next-generation robotics AI. They are developing agents that learn from large-scale multimodal data and improve through iterative fine-tuning, evaluation, and feedback. This hands-on role contributes to real systems and experiments that advance the capabilities of embodied AI agents.

The TL;DR
• Role: Internship
• Type: Full-time
• Location: In-person, Boston, MA

• Team: AI Team, working alongside researchers and engineers
• Mission: Assist in solving core problems in robotics AI, including data collection, model training, and evaluation.
• Tech Stack: PyTorch, TensorFlow, JAX

What You’ll Actually Do
• Data Curation: Assist with large-scale data collection and curation for robotics model training in real-world and simulation environments.
• Model Training: Help train and fine-tune multimodal models for embodied tasks, including vision, language, and vision-language-action.
• Evaluation: Run evaluations and ablation studies on robotics policies and algorithms to assess performance.
• Performance Analysis: Analyze model performance, identify specific failure modes, and propose actionable improvements.
• Tooling Development: Build and maintain tooling for experiment tracking, data pipelines, and evaluation benchmarks.

The Must-Haves
• Background: Currently pursuing a degree in Computer Science, Machine Learning, Robotics, or a related field, with a solid foundation in machine learning and deep learning concepts.
• Experience: Proficiency in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Skills: Strong problem-solving abilities, the capacity to work independently, and an interest in robotics, computer vision, or multimodal learning.
• Bonus: Experience with robotics simulation environments (e.g., MuJoCo, Isaac Sim, Gazebo); familiarity with reinforcement learning, imitation learning, or policy optimization; prior experience with data annotation, labeling pipelines, or dataset engineering; coursework or projects in computer vision, NLP, or multimodal systems.