Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance. Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world. Our team comes from Google DeepMind, Meta, and Cruise, and we're backed by CRV, First Round, and other leading investors.

The Role

Many believe that as long as a robot is “smart” enough, deploying it is easy. At Dyna, we know the truth: the real world is wild, unpredictable, and unforgiving. Landing breakthrough AI research into the field takes deep investment in infrastructure and applied research.

As we scale the number of deployed sites, the bottleneck is no longer whether a robot can perform a task once. It’s whether we can deploy, monitor, debug, and improve robots across many environments without every site becoming a one-off engineering project. Your job is to turn that messy, high-friction reality into durable engineering infrastructure. You’ll partner closely with our Operations team rather than selling to customers, with one goal: make deployment N+1 faster and more reliable than deployment N.

This is a cross-domain role spanning applied research on model stability, on-robot software, and the off-board systems and tooling that keep a fleet running.

What You Will Do

• Build the deployment platform. Design and ship internal tools and systems for site bring-up, robot configuration, fleet monitoring, remote diagnostics, data capture, failure triage, deployment checklists, and operational dashboards.
• Partner with Operations. Embed with the Ops team to understand field reality, identify which failures matter most, and turn manual workflows into reliable software. Standardize how new sites are launched so each deployment compounds on the last.
• Close the loop with the model team. Build the pipelines that route useful data and failure cases from the fleet back to research, and make it easier to compare performance across different environments and tasks.
• Applied research and engineering. Participate in applied research focused on ensuring model-level stability and consistency when deploying complex AI models into unpredictable real-world environments.
• End-to-end infrastructure architecture. Design and build highly reliable infrastructure that bridges on-robot application software with large-scale off-board computing and cloud operations.
• Bridge research and reality. Act as the technical bridge between our core AI research teams and our deployment reality, translating complex lab research into robust, field-ready systems.