Robotics Manipulation R&D Intern

About Create Me

Create Me is an AI robotics company pioneering automated manufacturing for soft materials, starting with apparel. Built on the belief that the Future of Fashion is Bonded™, we've developed a unified platform that combines advanced robotics, proprietary adhesive bonding, and Physical AI to deliver a new standard of precision, consistency, and speed.

By replacing stitch-based construction with digitally applied adhesives and automated material handling, our platform enables localized, on-demand production that reduces waste, shortens supply chains, and improves recyclability by design. Core technologies include Pixel™ micro adhesive bonding, the MeRA™ robotic assembly system, and Thermo(re) set™ reversible adhesive science. With more than 95 patents across robotics, adhesives, and Physical AI, we're defining the infrastructure for bonded manufacturing globally.

As a Robotics Manipulation R&D Intern, you'll work shoulder-to-shoulder with our robotics team on one of the hardest open problems in Physical AI: learning-based manipulation of highly deformable fabrics. This is a hands‑on research role centered on vision‑language‑action (VLA) models and modern robot learning pipelines — from data through training through real‑robot rollouts.

You'll work on meaningful slices of our learning stack: validating data collection pipelines, training and tuning policies, running them on real robots, and helping stand up our simulation pipeline for fabric manipulation.

This is a full‑time role for a limited, temporary term, and is not eligible for company benefits unless required by applicable law. The pay rate is hourly, and the role is non‑exempt (eligible for overtime).

This role is primarily onsite in our R&D Innovation Center in Newark, CA, with some work‑from‑home flexibility. The Create Me office is in Newark, CA and this role is not eligible for relocation assistance.

Human Demonstration Data → Real Robot Policies: Validate end‑to‑end pipelines that take human‑collected fabric manipulation data through to trained policies. Train VLA / imitation learning models (e.g., π0 / π0.5, ACT, diffusion policies) and roll them out on real robot hardware for fabric manipulation tasks (e.g., pick‑and‑place of fabric). Iterate on data quality, model architecture, and training recipe to push success rates upward.

Task Progress & Evaluation Models: Train and evaluate models for task progress estimation from human manipulation data. Explore architectures that can consume egocentric video and pose data and output progress / phase signals usable by downstream policies or evaluation harnesses.

Simulation Pipeline: Help stand up and validate a simulation pipeline for fabric manipulation — importing assets, tuning cloth physics to usable fidelity, setting up bimanual robot scenes, and training policies via imitation learning with exploratory RL work on top. Contribute to sim‑to‑real transfer experiments.

Experimentation & Tracking: Run disciplined experiments with proper tracking, dataset versioning, and clear ablations. Communicate findings crisply so the team can make decisions quickly.

Cross-Functional Collaboration: Partner with mechanical, hardware, and data teammates to unblock data issues, shape end‑effector iterations, and translate findings into the team's broader roadmap.

• Currently pursuing a Master's or PhD in Robotics, Computer Science, Machine Learning, EE/ME, or a related technical field.
• Strong foundation in modern robot learning — imitation learning and/or VLA models (π0 / π0.5, OpenVLA, RT-2, or similar). You should be comfortable reading recent papers in this space and reasoning about architecture and training tradeoffs.
• Hands‑on experience training and fine‑tuning deep learning models in PyTorch, including familiarity with practical issues: data loaders, mixed precision, distributed training, debugging training instability, evaluation harnesses.
• Strong Python skills. Comfortable working in a real codebase, not just notebooks.
• Experience working with real‑world robot data or deploying learned models on physical robots (course projects,…