Dyna Robotics builds 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 across multiple industries, and the frontier model has the top generalization and performance in the industry.

The Opportunity

Many believe that once a robot is smart enough, deployment is easy. At Dyna, the reality is different: the real world is wild, unpredictable, and unforgiving. Getting breakthrough AI research into the field takes deep investment in infrastructure and applied engineering.

As Dyna scales the number of deployed sites, the bottleneck is no longer whether a robot can perform a task once — it is whether the team can deploy, monitor, debug, and improve robots across many environments without every site becoming a one-off engineering project. This role turns that messy, high-friction reality into durable engineering infrastructure. You will partner closely with the Operations team 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'll Do

• •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
• •Embed with the Operations 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
• •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
• •Participate in applied research focused on ensuring model-level stability and consistency when deploying complex AI models into unpredictable real-world environments
• •Design and build highly reliable infrastructure that bridges on-robot application software with large-scale off-board computing and cloud operations
• •Act as the technical bridge between core AI research teams and deployment reality, translating lab research into robust, field-ready systems
• •Champion rigorous testing, continuous integration, and scalable software practices that keep systems running at the complexity of the physical world

You Should Have

• •3+ years of professional experience as a versatile software engineer with strong capabilities in both backend systems and broad application-level software
• •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
• •Proficiency in the Python ecosystem
• •Proven experience building robust, scalable software that spans multiple domains such as 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, or Azure)
• •Strong cross-domain communication skills and the ability to collaborate effectively with Operations, AI researchers, hardware engineers, and platform teams
• •Comfort with ambiguity, a genuine interest in operational pain, and motivation to build systems that compound
• •Bachelor's degree or higher in Computer Science, Computer Engineering, Robotics, or a related field

Nice to Have

• •Experience tackling model stability, consistency, and edge-case handling in real-world deployments
• •Prior work building internal tooling for fleet operations, hardware deployments, or large-scale field systems
• •Familiarity with Unity, C#, or VR development
• •Familiarity with web frontend technologies

Compensation and Benefits

Base Salary: $170,000 - $250,000, depending on experience, plus equity.