1.2 Multi-agent AI Research Engineer: Scalable Robot Fleet Coordination

Field AI is 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.

At Field AI, we are moving beyond single-agent autonomy—scaling AI coordination across fleets of robots in unstructured, high-risk environments. Our work in Field Foundation Models™ (FFMs) is enabling multi-robot decision-making, strategic coordination, and decentralized intelligence at unprecedented levels. From large-scale robotic deployments in complex environments to real-time tactical decision-making, we are pioneering multi-agent AI that is explainable, risk-aware, and field-ready.

We are seeking a Multi-Robot Intelligence Research Engineer to design and implement scalable algorithms for coordination, decentralized control, and game-theoretic decision-making in multi-robot systems. This role is at the intersection of robotics, AI, and mathematical game theory, pushing the boundaries of large-scale, real-world autonomy.

• Develop fundamental algorithms for multi-agent coordination (including differentiable game theory, mean-field control, and decentralized optimization) to enable fleets of autonomous robots to operate in real-world, high-stakes environments.
• Design computationally tractable formulations of multi-agent Nash equilibria, Stackelberg games, and cooperative decision-making strategies, ensuring robust and scalable decision-making across heterogeneous robotic teams.
• Build predictive models for multi-agent interaction dynamics, leveraging graph-based learning and control-theoretic formulations to drive efficient coordination in dynamic, adversarial, and uncertain settings.
• Develop distributed inference and control policies using neural PDEs, mean-field game-theoretic methods, and scalable stochastic optimization for real-time at-scale robotic interaction.
• Bridge theory with deployment—integrate multi-agent planning, auction-based task allocation, and decentralized multiagent reinforcement learning (MARL) into hardware-in-the-loop robotic systems operating at scale.
• Push the limits of explainability in multi-agent AI, ensuring tractability, convergence guarantees, and real-world feasibility while maintaining risk-aware and uncertainty-resolving decision-making.
• Collaborate across teams to transition multi-agent models from high-fidelity simulations to real-world deployments, working alongside robotics engineers, AI/ML researchers, and field roboticists to ensure seamless real-world operation.

• Ph.D. in Applied Mathematics, Game Theory, Control Theory, Computer Science, or a related field, with expertise in multi-agent decision-making and coordination algorithms.
• Deep understanding of game-theoretic methods—including differential games, Nash equilibria, mean-field games, and Stackelberg equilibria—with a focus on scalability and tractability.
• Experience with multi-agent RL (MARL) and distributed optimization for large-scale robotic coordination in imperfect information settings.
• Hands-on experience implementing multi-agent algorithms in real-time robotic or AI-driven systems, with exposure to hardware constraints, real-world latency, and stochastic disturbances.
• Proficiency in Python, C++, or Julia, with experience in optimization libraries (e.g., CVXPY, Gurobi, JAX), reinforcement learning frameworks (e.g., RLlib, Acme), and multi-robot simulators.
• Experience working with large-scale robotic coordination (e.g., drone swarms, autonomous fleets, or industrial automation systems) is a strong plus.
• Ability to transition theoretical insights into scalable, field-deployable systems, ensuring robustness under uncertainty and adaptability to real-world constraints.

Compensation and Benefits

Our salary range is generous ($70,000 - $300,000 annual), but we take into consideration an individual's background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience.  Also, while we enjoy being together on-site, we are open to exploring a hybrid or remote option.

Why Join Field AI?

We are solving one of the world’s most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models™ set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment.

You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field, winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals.

Be Part of the Next Robotics Revolution

To tackle such ambitious challenges, we need a team as unique as our vision — innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We’re seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators.

We are headquartered in always-sunny Mission Viejo (Irvine adjacent), Southern California and have US based and global teammates. 

Join us, shape the future, and be part of a fun, close-knit team on an exciting journey!

We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, martial status, mental or physical disability, or any other legally protected status.