About the team:
Mind Robotics is building robots that learn from real-world experience — and the quality of that experience starts with how we collect and process data. Our field data collection system puts capture rigs on the factory floor at manufacturing sites, generating the raw sensor data that trains everything downstream. Our teleoperations program turns human demonstrations into the high-quality data that grounds our AI models in physical reality. Both flow through annotation and quality control into our training platform.
About 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 teleop 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.
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 teleop 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