Robotics Research Data Scientist (Remote)

Crossing Hurdles; United States; On-site; USD 200,000 - 1,500,000; Full time

Job summary

Crossing Hurdles is seeking a Member of Technical Staff, Robotics Research to tackle data-driven robotics challenges in a remote setting. This full-time role focuses on designing datasets, annotations, and evaluation schemes to advance embodied AI and multimodal learning.

You will run experiments to identify data structures that improve model performance, define data schemas, and collaborate with leading AI labs to turn research needs into scalable data pipelines and products.

Qualifications

• Experience in robotics, computer vision, multimodal learning, embodied AI, or applied ML research.
• Strong understanding of robotics/physical AI and multimodal data.
• Ability to translate research problems into concrete datasets, experiments, and specifications.
• Strong technical communication and cross-functional collaboration skills.
• Degree in Robotics, CS, EE, ML, or related field, or equivalent experience.

Responsibilities

• Research the data needs of frontier robotics, world models, and physical AI systems.
• Design new datasets and collection methods across egocentric video, teleop, UMI, robot trajectories, sensors, and simulation.
• Define data schemas, annotations, ground truth, and evaluations for manipulation, navigation, spatial reasoning, and physical understanding.
• Run experiments to understand what data and structure most improve model performance.
• Work with leading AI and robotics labs to turn research needs into scalable data pipelines and products.
• Stay current on frontier research and shape robotics data strategy.

Skills

Robotics

Computer Vision

Multimodal Learning

Embodied AI

Applied ML Research

Education

Degree in Robotics, CS, EE, ML, or related field

Job description

Crossing Hurdles is seeking a Member of Technical Staff, Robotics Research to tackle data-driven robotics challenges in a remote setting. This full-time role focuses on designing datasets, annotations, and evaluation schemes to advance embodied AI and multimodal learning.

You will run experiments to identify data structures that improve model performance, define data schemas, and collaborate with leading AI labs to turn research needs into scalable data pipelines and products.