About the role
About Us
Parametric builds autonomous physical businesses. We're starting with a laundromat, designed from the ground up for autonomy. Dirty laundry in, folded clean laundry out. Rather than retrofit human workflows with robots, we redesign processes with robots as first class citizens.
About the Role
As a Machine Learning Research Engineer, you will architect the neural backbones that drive our robots. This is not just a "tuning" role; you will define how we apply state-of-the-art developments in Transformers and World Modeling to physical control problems.
You will work at the intersection of perception and action, designing novel algorithms that allow robots to understand complex scenes and execute precise tasks. You will own the full research-to-deployment loop: reading papers, prototyping in PyTorch, training at scale, and deploying to hardware.
What You’ll Do
- Architect Neural Policies: Design and train large-scale Transformer-based policies that integrate multimodal inputs (vision, proprioception) for end-to-end robotic control.
- Advance World Modeling: Develop predictive world models that allow agents to reason about future states and physical interactions, reducing the sample complexity of real-world training.
- Reinforcement Learning at Scale: Implement and refine advanced RL algorithms—specifically PPO, GRPO, and Q-Learning variants—to solve complex manipulation and navigation tasks.
- Vision Foundation Models: Leverage and fine-tune modern self-supervised vision backbones (e.g., DINOv2, SigLIP) to provide dense, semantic understanding of the robot's environment.
