Software Engineer, Applications

Join us to shape the next frontier of AI-driven robotics! Dyna Robotics makes general-purpose robots powered by a proprietary embodied AI foundation model that generalizes and self-improves across varied environments with commercial-grade performance. Dyna's robots have been deployed at customers across multiple industries. Its frontier model has the top generalization and performance in the industry. Dyna Robotics was founded by repeat founders Lindon Gao and York Yang, who sold Caper AI for $350 million, and former DeepMind research scientist Jason Ma. The company has raised over $140M, backed by top investors, including CRV and First Round.We're positioned to redefine the landscape of robotic automation. Position Overview Many believe that as long as a robot is "smart" enough, deploying it is easy. At Dyna Robotics, we know the truth: the real world is wild, unpredictable, and entirely unforgiving. Without deep investment into the underlying infrastructure and applied research, there is no easy way to land breakthrough AI research into the field. As an Applied AI Engineer in deployment you will be the critical link that brings our general robotic intelligence into reality at scale. This is a cross-domain engineering role, not a pure field deployment position. You will straddle the line between research and infrastructure, participating in applied research projects that ensure model-level stability and consistency, while simultaneously building the vast, end-to-end infrastructure required to keep our entire software stack reliable. Here, "infrastructure" is not just a backend system, it represents a massive domain, building the connective tissue that spans from large-scale, off-board computing operations down to the critical application-level software executing natively on the robots themselves. You will ensure that as our robots step out of the lab and into the wild, they are supported by software architectures as advanced and robust as the AI that powers them. What You Will Do Applied Research & Engineering: Participate in applied research work 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 spans a broad domain; bridging on-robot application software with large-scale off-board computing and cloud operations. Bridge the Gap: Act as the technical bridge between our core AI research teams and our deployment reality, translating complex lab research into robust, field-ready systems. Fleet Management at Scale: Develop the comprehensive systems, data collection pipelines, and tooling necessary to manage, monitor, and support a large-scale robot fleet. Engineering Excellence: Champion rigorous testing, continuous integration, and scalable software practices that ensure our systems can handle the immense complexity of the physical world. What You’ll Bring Bachelor’s degree or higher in Computer Science, Computer Engineering, Robotics, or a related field. Strong Computer Science foundations: Deep, first-principles understanding of systems architecture, software design patterns, and application-level development. You understand how systems work under the hood, not just how to string APIs together. At least 3 years of professional experience as a versatile software engineer, with strong capabilities in both backend systems and broad application-level software. Proficient in Python ecosystem. Proven experience building robust, scalable software that spans multiple domains (e.g., bridging cloud backend services with edge devices, physical hardware, or complex data collection systems). Experience with containerization, orchestration (Docker, Kubernetes), and cloud architecture (AWS, GCP, Azure). Highly adaptable and comfortable working across a varied tech stack. Strong cross-domain communication skills, with the ability to collaborate effectively with AI researchers, hardware engineers, and platform teams. Demonstrated passion for the intersection of AI research and wide-scale infrastructure, specifically for robotics or autonomous systems.