Overview
Dexmate is building the foundation for physical AI — a unified platform that combines high-quality robotic hardware with a universal Physical AI OS, making robots as easy to build
Full job description
Dexmate is building the foundation for physical AI — a unified platform that combines high-quality robotic hardware with a universal Physical AI OS, making robots as easy to build and deploy as software. Today, robotics is fragmented, slow, and closed: most builders are forced to reinvent the same stack again and again, and most ideas never make it past the prototype stage. We exist to change that. Our mission is to democratize robotics by lowering the barrier to entry, delivering a plug-and-play platform for developers, researchers, and enterprises, and cultivating an open ecosystem that accelerates the evolution of physical AI. If you want to help shape the next layer of human capability — and believe the future of robotics should be built together, not in isolation — we’d love to build it with you. The Role We are looking for a Software Engineer to build the infrastructure that powers data, models, and AI runtime across Dexmate’s Physical AI platform. You will help move models and robot data from experimentation into scalable, reliable production systems. Responsibilities -Build scalable infrastructure for robot data ingestion, processing, storage, and dataset lifecycle management. -Develop model infrastructure for training, evaluation, versioning, deployment, and serving. -Build agent runtime systems including execution, orchestration, memory, and model integration. -Develop model evaluation and experimentation infrastructure for quality, regression, and performance measurement. -Improve reliability, scalability, observability, and cost efficiency across data and model systems. Requirements -5+ years of software, data infrastructure, ML infrastructure, or distributed systems experience. -Strong experience with large-scale data pipelines, distributed computing, model serving, or ML platforms. Experience with cloud infrastructure, storage, containers, and orchestration. -Familiarity with model training, inference, evaluation, and experimentation workflows. -Robotics, autonomous driving, multimodal data, or AI infrastructure experience is a plus.
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