Remote | VP of Robotics Research — $1,500,000–$4,000,000/year

24-Mag•New York, NY

2d•Remote

About The Position

The VP of Robotics Research will define and execute research strategy for robotics data, embodied AI, and robot learning. The role sets research priorities across perception, manipulation, control, and data-centric learning, translating emerging research into scalable technical programmes while maintaining a clear technical vision and participating in experimentation. The position defines how robotics data is collected, represented, validated, and delivered; develops multimodal datasets and scalable collection strategies; and establishes task taxonomies, annotation schemas, and quality-control processes. It leads research in robot perception and manipulation, evaluates learning and control approaches, and connects research results with scalable engineering implementations. The VP establishes benchmarks and evaluation metrics for robotics tasks, uses evaluation findings to guide research priorities, and builds, mentors, and grows a high-performing Robotics Research team. The role partners with AI researchers, robotics engineers, product, and operations to translate research and client objectives into data-pipeline specifications and production-ready robotics data solutions. Strong technical leadership combined with hands-on robotics research experience is central. Priorities, datasets, and technical approaches may evolve as research requirements develop. This is a full-time, fully remote opportunity available through 24-MAG LLC. Compensation is $1,500,000–$4,000,000 per year. Work must be completed without using confidential, proprietary, unpublished, client-sensitive, or otherwise restricted information belonging to any employer, research laboratory, university, robotics company, client, collaborator, or other third party.

Requirements

Significant experience in robotics research, embodied AI, robot learning, or robotics software engineering.
Proven experience leading or mentoring technical or research teams.
Deep understanding of robotics data pipelines and multimodal datasets.
Strong expertise in perception, manipulation, control, imitation learning, reinforcement learning, or robotics foundation models.
Experience designing and evaluating large-scale robotics datasets.
Ability to translate research into scalable systems.
Excellent written and verbal communication skills.
Work must be completed without using confidential, proprietary, unpublished, client-sensitive, or otherwise restricted information belonging to an employer, research laboratory, university, robotics company, client, collaborator, or other third party.

Nice To Haves

A master's or PhD in Robotics, Computer Science, AI, Electrical Engineering, or a related field is highly valued.
Publications at CoRL, RSS, ICRA, IROS, NeurIPS, ICML, CVPR, or comparable venues are advantageous.
Experience with ROS/ROS2, Isaac Lab, Isaac Sim, MuJoCo, Habitat, ManiSkill, or similar environments is valuable.
Open-source robotics contributions or significant embodied-AI research are advantageous.

Responsibilities

Define and execute research strategy across robotics data, embodied AI, and robot learning.
Set priorities across perception, manipulation, control, and data-centric learning, and translate emerging research into scalable technical programmes.
Define how robotics data is collected, represented, validated, and delivered.
Design multimodal datasets covering perception, actions, trajectories, and environment state.
Develop scalable collection strategies using teleoperation, demonstrations, simulation, autonomous exploration, and real-world deployments.
Establish task taxonomies, annotation schemas, and quality-control processes.
Lead research involving robot perception and manipulation; evaluate imitation learning, reinforcement learning, control, and foundation-model approaches.
Analyze performance limitations and sources of failure, and connect research findings with scalable engineering implementations.
Establish benchmarks, metrics, and success criteria for robot-learning performance and use evaluations to guide research priorities.
Build, mentor, and grow a high-performing Robotics Research team; foster scientific rigor, curiosity, and rapid experimentation.
Promote technical documentation and knowledge sharing while balancing long-term research priorities with practical delivery requirements.
Partner with AI researchers, robotics engineers, product, and operations teams to translate research and client objectives into data-pipeline specifications.
Operationalize research into production-ready robotics data solutions and communicate technical trade-offs and requirements across teams.