About Mind:
Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor. We believe the hardest problems in AI are solved when researchers and engineers are hands-on with the physical world every day - and we're looking for people who are passionate about robotics, value ownership, and are excited to tackle difficult problems. Join us if you want to move beyond digital intelligence and put intelligence into motion.
About the team and the role:
As an Application Engineer, you’ll be one of the founding engineers on the team that owns this application layer, reporting to our Head of Application Engineering. The systems work today; your job is to help harden and productionize them for scale — from 10 capture stations at one site to hundreds across multiple OEM plants, and from a handful of tele-operation stations to a multi-shift fleet. This is early, hands-on, 0-to-1 engineering: you’ll ship code that runs on real hardware in real factories, and see its effect on robot behavior.
Responsibilities:
Build the field capture stack — edge software for our data collection rigs: device management, sensor orchestration, and real-time data quality monitoring that holds up on a factory floor.
Ship the tele-operations stack — collection, evaluation, and operator tooling for our teleoperation stations, including metrics, task management, and the workflows our robot operators use every day.
Streamline annotation methods/models to increase labelling efficiency.
Make deployment repeatable — contribute to tooling and configuration systems that make onboarding a new manufacturing site a config change, not a custom engineering project.
Support the field — debug issues on live systems, instrument for observability, and work directly with site operations staff and robot operators who depend on your software.
Requirements:
3+ years of software engineering experience building production systems.
Strong programming fundamentals and comfort working across the stack — edge devices, services, and data pipelines.
Experience with at least one of: real-time data pipelines, edge computing, device or fleet management, robotics or sensor systems.
Bias for ownership: you’ve taken features or systems from prototype to production and supported them in the field.
Clear communication and close collaboration with product, research, and operations partners.
Hands-on experience with sensor data (video, depth, IMU, force/torque) and the infrastructure to process it at scale is a plus.
Background in robotics, autonomous vehicles, industrial IoT, or teleoperation systems is a plus.
Exposure to manufacturing or other industrial environments is a plus.
Familiarity with ML data workflows (datasets, labelling, evaluation) is a plus.