Machine Learning Research Engineer

Parametric is building robots to reliably automate frontline physical labor, starting with laundry folding. As a Machine Learning Research Engineer, you will architect the neural backbones that drive our robots and own the full research-to-deployment loop, designing novel algorithms for robotic control and understanding complex scenes.

Responsibilities:

• 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
• Reward Engineering: Design robust reward modeling architectures that align agent behavior with high-level task goals, utilizing techniques like inverse reinforcement learning or preference-based learning
• High-Performance Engineering: Write production-grade PyTorch code. You may also explore or implement components in JAX for high-throughput simulation and training