About Ndimensions Labs

At Ndimensions, we're inventing the infrastructure for next-generation robotics AI systems. To train intelligent agents that can understand and act in the world, we need world-class experimentation, data capture, and simulation systems.

We're looking for an engineering leader in robotics experimentation and data operations to join our hardware, simulation, and data collection teams. This role is essential to ensuring the model teams receive high-quality, well-structured, and diverse data to power robust AI behaviors.

You'll help design and run real-world and simulated experiments, calibrate hardware, optimize data pipelines, and ensure reproducibility and scale. Your work will directly impact the quality of the training signals that power our vision-language-action agents.

What You'll Do

• Design and build experimental setups across real-world robotic systems and simulation environments.
• Calibrate and validate hardware configurations (e.g., robot arms, sensors, cameras) to ensure data quality.
• Coordinate and execute data collection cycles, including scripted procedures, teleoperation, or autonomous behaviors.
• Collaborate closely with simulation and hardware teams to instrument, test, and iterate on new environments and agents.
• Build software tooling to streamline the collection, annotation, and ingestion of robotic experience data.
• Work with the model teams to define data requirements, format specifications, and feedback loops.

What We're Looking For

• Strong engineering background in robotics systems, embedded software, or simulation.
• Experience setting up and running labs involving robot arms, cameras, sensors, or motion capture systems.
• Familiarity with ROS, Python, C++, and common robotics middleware or simulator stacks (e.g., Gazebo, Isaac Sim, Mujoco).
• Practical understanding of data capture, storage, and preprocessing pipelines in ML workflows.

Bonus (Not Required)

• Experience with cloud robotics pipelines or large-scale data annotation.
• Prior work in robotics labs, autonomous systems, or robot learning environments.
• Publications or projects related to robotic data collection, sim-to-real, or RLHF for robotics.