Robotics Hardware & Data Engineer

We’re looking for a Robotics Hardware / Data Engineer to design the hardware and software interfaces that power robot teleoperation and large-scale data collection. You’ll own the systems that let human operators control robots and that turn every session into clean, high-fidelity training data. This is a hands-on role spanning mechatronics, embedded software, and ML data pipelines - ideal for someone who’s equally comfortable with a motor driver and a training loop.

• Design and build hardware interfaces for robot teleoperation - mapping human input to robot control in real time.
• Develop data-collection systems that record synchronized, high-quality sensor, actuation, and video streams for downstream robot learning.
• Work across the stack: motor control, signal processing, and low-latency communication between operator hardware and robots.
• Build the tooling and pipelines that validate, clean, and structure collected data for ML training.
• Partner across engineering, operations and research teams to make sure the data we capture matches what models actually need, and feed learnings back into hardware and catalog design.
• Prototype quickly, debug real hardware, and iterate on rigs, controllers, and capture setups.

Robotics, Computer Engineering, Training/Teaching, Spatial Data, Artificial Intelligence (AI), Robotics, Data Collection, Computer Engineering, Hardware Design, Motor Control Systems, * Background in mechanical/mechatronics engineering, robotics, or a closely related field.

• Hands-on experience with motors, robotic control, and signal processing.
• Familiarity with machine learning and robot learning - you understand how data becomes a trained policy.
• Comfort working across hardware and software: embedded systems, real-time control, and data pipelines.
• A builder’s instinct: you like making physical systems work reliably in the real world.

Nice to have:

• Experience with teleoperation systems or large-scale robot data collection.
• Familiarity with the robot learning research community (e.g. CoRL) and modern imitation/RL approaches.
• Experience designing data schemas or catalogs for ML training.

Calder is solving the data bottleneck for embodied AI and world models. Today’s AI models lack the real-world, 4D data needed to reason on spatial problems and operate in physical environments. We capture, process, and deliver high-fidelity human-generated spatial data at scale, providing the substrate that trains the next generation of generalizable models for manipulation, navigation, and interaction.