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Path Robotics is building intelligent robotic systems to address labor shortages in manufacturing and industrial sectors. This role bridges AI research and production, focusing on the software infrastructure that enables machine learning models to move from experimentation to real-world deployment on robotic systems.
You will own the design and maintenance of software infrastructure supporting robot learning, including model training pipelines, experiment tracking, versioning systems, and deployment frameworks. Key responsibilities include developing data pipelines for collecting, processing, and curating large-scale robotic sensor and telemetry data; supporting AI model deployment onto robotic hardware with simulation, integration, runtime monitoring, and feedback loops from field data; building internal tools that accelerate AI engineering workflows across dataset exploration, testing, evaluation, and productionization; and partnering closely with AI researchers and robotics engineers to translate model requirements into robust, scalable software systems.
This is a systems-oriented role for someone who understands both machine learning workflows and production software engineering. You'll work across the full stack from data collection and testing through deployment on real systems, requiring comfort with containerized workflows, GPU compute, and Linux environments.
REQUIREMENTS:
- 3–6 years of software engineering experience with exposure to ML infrastructure, MLOps, or AI-enabled systems
- Strong production software skills in Python; ideally also C++, ROS, or robotics software stacks
- Experience with PyTorch and practical understanding of software systems for AI model development
- Comfort with data pipelines, simulation environments, containerized workflows, and GPU compute in Linux environments
- Experience working across AI and robotics domains, supporting the full path from data collection through deployment on real systems
