Software Engineer, Applications

About the role

You will own the design and execution of deployment infrastructure that turns cutting edge embodied AI research into reliable, repeatable operations at scale. You will own the end-to-end responsibility for building the systems that enable robots to be deployed, monitored, debugged, and improved across many diverse sites without requiring bespoke engineering for each location. You will own the interface between the research lab and the physical world, translating model behavior into robust, field-ready software stacks. You will own the partnership with our Operations team to codify manual workflows into automated, compounding engineering processes. You will own the durability and reliability of the fleet tooling that ensures every deployment builds on the last. You will own the design of on-robot and off-board systems that together form a cohesive platform for real-world operation. You will own the implementation of diagnostic, data capture, and failure triage systems that keep our robots running and our learning cycle tight.

Key facts

What you'll do

• Build the deployment platform that powers site bring-up, robot configuration, fleet monitoring, remote diagnostics, data capture, failure triage, deployment checklists, and operational dashboards.
• Partner with Operations to embed in field workflows, identify the most critical failures, and convert manual processes into reliable software that standardizes new site launches.
• Close the loop with the model team by constructing pipelines that route useful data and failure cases from the fleet back to research, enabling clear performance comparison across environments and tasks.
• Conduct applied research focused on ensuring model-level stability and consistency when deploying complex AI models into unpredictable real-world environments.
• Design and build end-to-end infrastructure that bridges on-robot application software with large-scale off-board computing and cloud operations in a cohesive architecture.
• Act as the technical bridge between core AI research teams and deployment reality, translating complex lab research into robust, field-ready systems that Operations can rely on.
• Champion engineering excellence through rigorous testing, continuous integration, and scalable software practices that handle the immense complexity of the physical world.
• Define and maintain deployment checklists and operational dashboards that make cross-site variability visible and manageable for both engineers and operators.
• Implement remote diagnostics and failure triage tooling that reduces downtime and accelerates the feedback loop between field and model teams.
• Enable data capture strategies that support long-term model improvement while preserving operational privacy and system reliability in customer environments.

Requirements

• Hold a Bachelor's degree or higher in Computer Science, Computer Engineering, Robotics, or a related field.
• Demonstrate strong CS foundations with a deep, first-principles understanding of systems architecture, software design patterns, and application-level development.
• Bring at least 3 years of professional experience as a versatile software engineer with strong capabilities in both backend systems and broad application-level software.
• Show proficiency in the Python ecosystem across libraries, frameworks, and tooling.
• Provide proven experience building robust, scalable software that spans multiple domains such as cloud backend services, edge devices, physical hardware, or complex data collection systems.
• Exhibit experience with containerization, orchestration using Docker and Kubernetes, and cloud architecture on platforms such as AWS, GCP, or Azure.
• Display high adaptability and comfort working across a varied technology stack as requirements and environments evolve.
• Communicate effectively across disciplines to collaborate with Operations, AI researchers, hardware engineers, and platform teams.
• Show comfort with ambiguity, a deep interest in operational pain, and motivation to build systems that compound in value over time.
• Demonstrate excitement for working close to production robotics and turning heroic on-site effort into durable, reusable engineering.
• Demonstrate a proven passion for the intersection of AI research and wide-scale infrastructure, specifically in the context of robotics or autonomous systems.
• Commit to upholding the technical rigor, reliability, and mutual respect required to build technology for the real world.
• Be an equal opportunity ally committed to building a diverse team as diverse as the environments our robots inhabit.

Nice to have

• Bring experience tackling model stability, consistency, and edge-case handling in real-world deployments of AI systems.
• Show prior work building internal tooling for fleet operations, hardware deployments, or large-scale field systems that span many locations.
• Have familiarity with Unity, C#, and VR development for simulation or visualization use cases.
• Have familiarity with web frontend technologies to support dashboards, configuration interfaces or debugging tools.

Practical notes

This role is full-time and based in Redwood City, California. We are an equal opportunity employer committed to technical rigor and mutual respect.