Senior Robotics Engineer (Federal)

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

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About this role

Senior Robotics Engineer (Federal) Irvine, CA Who are We? Field AIis transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications. Learn more at https://fieldai.com. About the Job At FieldAI, we build autonomous robotic systems that operate in demanding, real-world environments where tight integration between hardware and software is critical. We're looking for a Senior Robotics Engineer to lead the design, implementation, and deployment of advanced control systems and collaborative behaviors for our autonomous vehicle fleet. In this role, you will serve as a key planning and controls expert, solving complex challenges involving operating in unstructured environments. You will own the evaluation and implementation of advanced control schemes, ensuring computationally constrained systems operate reliably across new and existing robotic platforms. Furthermore, you will drive the development of multi-agent operations and fleet coordination behaviors. This is a highly impactful role for an engineer with deep expertise in control theory and multi-agent autonomy who can translate advanced research into robust, field-ready solutions. 1. Vehicle Planning and Control Identify and model the dynamic behavior of autonomous vehicles to establish accurate baselines for motion planning. Adapt and train control models to transition between diverse vehicle platforms. Develop, optimize, and implement robust control stacks

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