Mind Robotics is building intelligent robotics for industrial deployment. We believe the fastest path to broadly capable robots is through clearly defined, high-impact environments. We are starting where the need is most acute and the environment is most exacting: the factory floor.

Role Summary

This role bridges the gap between human intent and robotic embodiment, transforming human demonstrations into high-fidelity data that grounds our models in physical reality. You will own the near-term mechanism for acquiring demonstration and recovery data, treating the entire data collection process as a product. Your mission is to build a platform that validates hardware designs, evaluates trained policies, and generates the corner-case data necessary for model stability.

Key Responsibilities

Infrastructure & Experience Design

• Teleoperation "Cockpits": Design and build intuitive control interfaces and workflows that allow human operators to teach robots complex tasks seamlessly.

• Hardware Validation: Utilize teleoperation as a rapid hardware iteration tool, providing immediate feedback on gripper designs, arm reach, and field-of-view constraints.

• Technological Innovation: Research and implement cutting-edge technologies, including VR, haptics, and ultra-low-latency streaming to enhance operator intuition.

Data Strategy & Fidelity

• High-Fidelity Capture: Optimize data pipelines to ensure human movements translate perfectly into robot-learnable data, accounting for nuances in kinematics and latency.

• Recovery Data Program: Develop strategies to capture "recovery data" by having operators take over when policies fail, creating high-value datasets for edge-case training.

• Sim-to-Real & Video: Own the strategic roadmap for Sim-to-Real data pipelines and passive video capture integration from the factory floor.

Performance & Value Measurement

• KPI Development: Establish and track metrics for the teleoperation program, including data collection throughput, "cost-per-hour" of useful data, and data-cleansing efficiency.

• Data Value Attribution: Implement frameworks to measure the specific value of captured data, utilizing concepts like Shapley values to determine how much each data source contributes to model improvement.

• Quality Monitoring: Monitor the output of the teleoperation engine to ensure data is useful for training, focusing on "Quality over Quantity".

Qualifications

• Background: Specialist in Robotics, Human-Computer Interaction (HRI), or Gaming Infrastructure.

• Technical Expertise: Deep understanding of latency, kinematic fidelity, and the challenges of mapping human motion to robotic embodiments.

• Strategic Mindset: Ability to treat data collection as a product, balancing immediate operational needs with long-term scaling goals.

• Experience: Proven track record in designing complex hardware-software interfaces or managing large-scale data collection environments.