Senior/Staff Software Engineer, Robotics Platform

FieldAI ’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.

We’re building the software platform that enables FieldAI’s foundation models to run reliably and efficiently across a growing range of robots, hardware configurations, and real-world applications. As a founding member of the Robotics Platform team, you’ll help define the architecture, interfaces, and runtime systems that make our autonomy stack portable across embodiments while delivering the performance and reliability required for deployment in the physical world.

This role sits at the intersection of robotics, distributed systems, middleware, and performance engineering. You’ll work across the full system—from APIs and subsystem boundaries to communication middleware, runtime execution, simulation, and deployment on real hardware—solving the foundational problems that allow autonomy teams to move faster without coupling themselves to a particular robot or hardware stack.

A key part of the role is defining how robots describe themselves to the rest of the system. You’ll design the models and interfaces used to represent robot capabilities, current state, and intent so that dashboards, autonomous agents, fleet-management systems, and other services can reason consistently about what a robot can do, what it is doing, and what actions are valid. These abstractions need to remain useful as robots gain new sensors, behaviors, embodiments, and autonomy capabilities.

At the same time, you’ll tackle the systems challenges of making sophisticated robot intelligence practical at the edge: designing clean abstractions without sacrificing performance, moving large amounts of data efficiently through constrained systems, understanding latency and reliability end to end, and building infrastructure that behaves predictably outside the lab.

You’ll have significant influence over the technical direction of the platform and will work closely with autonomy, hardware, simulation, infrastructure, product, and deployment teams to turn cross-cutting requirements into durable systems and interfaces. The systems you build will directly determine how quickly FieldAI can bring its intelligence to new robots, integrate with fleet and operational tooling, and deploy new applications in the real world.

What You'll Get to Do

• Architect the software platform for physical AI—the foundation on which our autonomy runs across legged robots, ground vehicles, humanoids, and future embodiments
• Define the interfaces and contracts between major subsystems, and steward them as versioned, well-documented APIs that internal teams and external partners can depend on
• Design the abstractions that describe a robot’s capabilities, state, and intent , creating a consistent contract between on-robot autonomy and higher-level systems such as dashboards, autonomous agents, mission planners, and fleet-management platforms
• Develop mechanisms for discovering and tracking robot capabilities dynamically as hardware, software, sensors, and autonomy modules change, enabling higher-level systems to determine what missions and behaviors a particular robot can support
• Drive abstractions that decouple our intelligence from specific robots, sensors, compute platforms, and communication architectures, accelerating integration onto new embodiments
• Own platform performance end to end, from real-time execution on the robot through distributed communication and cloud connectivity, and establish the standards for latency, throughput, resource utilization, and reliability in the field
• Build and evolve the middleware and runtime infrastructure that moves high-bandwidth sensor data, model inputs and outputs, commands, and state throughout the system
• Develop tools and methodologies for profiling, tracing, benchmarking, and understanding system behavior across simulation and physical hardware
• Partner with autonomy, hardware, simulation, infrastructure, product, and deployment teams to identify systemic bottlenecks and turn recurring integration problems into reusable platform capabilities
• Set the technical direction for platform engineering at FieldAI: write designs, build consensus across teams, raise the engineering bar through review and mentorship, and help grow the team

What You Have

• Education: B.S. or higher in Computer Science, Robotics, Electrical Engineering, or a related technical field, or equivalent practical experience
• Experience: 5+ years building production robotics or systems software, including hands-on ownership of platform, framework, or infrastructure software that other engineering teams built on top of
• Distributed robotic systems: experience architecting distributed systems involving multiple autonomous agents, processes, computers, or robots, with a strong understanding of the failure modes that emerge outside controlled environments
• Deep ROS 2 and middleware expertise: fluency well beyond application-level use, with a strong command of the distributed systems fundamentals underneath—pub/sub and RPC semantics, quality of service, serialization, discovery, transport, and how these behave on constrained compute and imperfect networks
• Robot capability and intent modeling: experience designing abstractions that describe what a robot can do, what resources or capabilities are currently available, and what the robot is trying to accomplish. You understand how these contracts support higher-level systems such as dashboards, agents, mission planners, and fleet-management platforms
• API and abstraction design: a track record of designing versioned interfaces, frameworks, or SDKs that evolved successfully across multiple consumers. Public examples such as open-source interface packages, SDKs, specifications, design proposals, or standards contributions are a strong plus
• Modern C++ and Python: expert-level proficiency, with a strong commitment to writing code and libraries that other engineers can understand, extend, and depend on
• Systems and performance engineering: experience with Linux on embedded or edge compute, profiling and tracing, concurrency, memory and compute constraints, and techniques for high-throughput, low-latency data movement
• Production track record: software you have written or architected running on real hardware in production, along with the operational judgment that comes from deploying it, debugging failures in the field, and supporting the teams and customers who depend on it
• Judgment and independence: ability to take an ambiguous, cross-team technical problem, determine the right scope and tradeoffs, and drive it to a shipped outcome with minimal direction
• Communication and collaboration: able to write design documents that create alignment, navigate teams with competing priorities, and deliver systems incrementally rather than holding out for a theoretically perfect architecture