Remote | Member of Technical Staff, Robotics Research — $500,000–$1,500,000/year

24-Mag•New York, NY

5d•Remote

About The Position

We are sharing a full-time opportunity for an experienced robotics researcher with strong expertise in robotics data, robot perception, multimodal learning, embodied AI, schema design, and robotics R&D to help design the datasets, collection methods, and evaluation frameworks that support next-generation robot learning and physical AI systems. The role sits at the intersection of robotics, embodied AI, and data. The successful candidate will translate frontier research questions into scalable data strategies, experimental frameworks, and structured datasets spanning video, sensors, trajectories, teleoperation, simulation, and other modalities used in modern robotics research.

Requirements

2–5+ years of experience in robotics, computer vision, multimodal learning, embodied AI, applied ML research, or a closely related field
Strong understanding of robotics, physical AI, and multimodal data
Ability to translate research questions into datasets, experiments, schemas, and technical specifications
Strong technical communication and cross-functional collaboration skills
Degree in Robotics, Computer Science, Electrical Engineering, Machine Learning, or a related field, or equivalent practical experience

Nice To Haves

Experience at a leading robotics, AI, autonomous-systems company, or research lab is highly valuable
Hands-on experience with embodied AI datasets, imitation learning, teleoperation, or robot-learning pipelines is advantageous
Experience designing or working with multimodal robotics datasets involving video, sensors, trajectories, or simulation is highly relevant
Familiarity with robotics hardware, sensor integration, or rapid prototyping is beneficial
Experience with data collection or evaluation for manipulation, navigation, spatial reasoning, or world models is a strong plus

Responsibilities

Robotics Data Research: Research the data requirements of frontier robotics, embodied AI, world models, and physical AI systems. Identify which forms of data are most useful for improving robot learning and perception. Analyze how dataset composition, structure, and diversity affect model performance. Translate emerging research needs into concrete data strategies. Help define scalable approaches to robotics data acquisition and evaluation.
Dataset & Collection Method Design: Design datasets and collection methodologies for advanced robotics applications. Work across egocentric video, teleoperation, UMI, robot trajectories, sensor data, and simulation. Define collection protocols that support repeatability, scalability, and research quality. Develop specifications for diverse physical and simulated environments. Evaluate trade-offs across different data-collection approaches.
Schema, Annotation & Evaluation Design: Define data schemas, annotation structures, and ground-truth standards. Create evaluation frameworks for manipulation, navigation, spatial reasoning, and physical understanding. Specify metadata and labels needed for multimodal robotics datasets. Design consistent structures across video, sensor, trajectory, and simulation data. Ensure datasets are suitable for model training, benchmarking, and research experimentation.
Experimentation & Model Performance Analysis: Run experiments to determine which data structures and collection approaches improve model performance. Evaluate the impact of different modalities, annotations, and dataset compositions. Develop hypotheses around robot-learning and perception performance. Interpret experimental results and recommend changes to data strategy. Support iterative improvement of data pipelines and research methodology.
Robotics R&D Collaboration: Work with advanced AI and robotics teams to translate research problems into data products. Collaborate across research, engineering, operations, and data functions. Communicate experimental findings and technical trade-offs clearly. Help convert frontier robotics research into scalable operational workflows. Stay current with developments in robotics, embodied AI, world models, and physical AI.