Applied AI Engineer, Physical AI platform

Job details

Work model

On-site

Salary

Not listed by source

Location

Fremont Office

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How this role compares at Dexmate

Dexmate has 39 live roles in MeritLog’s job list across 10 job types, and 24 of them are in engineering. 0 of those jobs list a pay range. That is 0%.

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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 an Applied AI Engineer to design, build, and ship AI-powered features across Dexmate’s software and Physical AI platform. You will turn foundation models into production agents that can reason, use tools, take actions, and reliably interact with users and robots.   Responsibilities -Design and deploy production-grade AI agents with tool calling, multi-step workflows, context, and memory. -Build chatbot and agent experiences across digital and Physical AI applications. -Develop evals, guardrails, fallback, and observability for reliable agent behavior. -Integrate LLMs, multimodal models, APIs, and platform tools into end-to-end workflows. -Own AI features from system design through implementation, rollout, and production monitoring.   Requirements -3+ years of software or ML engineering experience. Hands-on experience building production LLM or agentic applications. -Experience with tool calling, RAG/context systems, prompting, orchestration, or evaluation. -Strong Python and software engineering fundamentals. Experience with multimodal models, VLM/VLA, or robotics is a plus.

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