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

24-Mag•New York, NY

20h•Remote

About The Position

This full-time, fully remote role focuses on robotics data research and the design of datasets, collection methods, and evaluation frameworks for next-generation robot learning and physical AI systems. The position sits at the intersection of robotics, embodied AI, and data, translating research questions into scalable data strategies, experimental frameworks, and structured datasets spanning video, sensors, trajectories, teleoperation, simulation, and other modalities. The work includes researching data requirements for frontier robotics, embodied AI, world models, and physical AI; designing datasets and repeatable collection methodologies; defining schemas, annotations, ground-truth standards, and evaluation frameworks; and running experiments to assess how data structures and collection approaches affect model performance. The role collaborates with research, engineering, operations, and data teams, and may cover manipulation, navigation, spatial reasoning, perception, and world-model research. Technical priorities and research scope may evolve as robotics and physical-AI initiatives develop. The opportunity is available through 24-MAG LLC, which connects experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

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.
Strong expertise in embodied AI, perception, robot learning, or robotics data.

Nice To Haves

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

Responsibilities

Research data requirements for frontier robotics, embodied AI, world models, and physical AI systems.
Identify data forms that can improve robot learning and perception, and analyze how dataset composition, structure, and diversity affect model performance.
Translate emerging research needs into concrete data strategies and help define scalable robotics data acquisition and evaluation approaches.
Design datasets and collection methodologies for advanced robotics applications across egocentric video, teleoperation, UMI, robot trajectories, sensor data, and simulation.
Develop repeatable, scalable collection protocols and specifications for diverse physical and simulated environments; evaluate trade-offs among collection approaches.
Define data schemas, annotation structures, ground-truth standards, metadata, and labels for multimodal robotics datasets.
Create evaluation frameworks for manipulation, navigation, spatial reasoning, and physical understanding, ensuring datasets support training, benchmarking, and research experiments.
Run experiments to determine which data structures and collection approaches improve model performance; evaluate modalities, annotations, and dataset composition.
Develop hypotheses about robot-learning and perception performance, interpret experimental results, recommend data-strategy changes, and support iterative improvement of data pipelines and research methodology.
Collaborate with AI and robotics teams and cross-functional partners to translate research problems into data products and scalable operational workflows.
Communicate experimental findings and technical trade-offs clearly and stay current with developments in robotics, embodied AI, world models, and physical AI.