Application Engineer

Mind Robotics Inc.; Palo Alto (CA); On-site; USD 100,000 - 130,000; Full time

Job summary

Mind Robotics Inc. is seeking an Application Engineer to join their team. You will play a pivotal role in building and scaling systems for data collection and teleoperations in robotic applications. Your obligations include developing the field capture stack, enhancing teleoperation systems, and streamlining data annotation methods.

Ideal candidates have robust software engineering backgrounds, with at least 3+ years of experience in building production systems. Prior exposure to robotics or autonomous systems and familiarity with data processing workflows are advantageous.

Qualifications

• 3+ years of software engineering experience building production systems.
• Experience with real-time data pipelines or robotics.
• Familiarity with ML data workflows is a plus.

Responsibilities

• Build the field capture stack for data collection rigs.
• Ship the teleoperation stack for operator tooling.
• Streamline annotation methods to increase labelling efficiency.
• Make deployment repeatable for new manufacturing sites.
• Debug issues on live systems with site operations staff.

Skills

Software engineering experience

Programming fundamentals

Real-time data pipelines

Edge computing

Robotics or sensor systems

Job description

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