Job description

FieldAI is transforming how robots interact with the real world. Our growing R&D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use. FieldAI is seeking passionate engineers to play a vital role in the development of multi-robot simulation and fleet orchestration. You will work with a focused team in a fast-paced environment to build simulation environments where robots operate, coordinate, and adapt together in realistic scenes, enabling the company to develop and validate fleet orchestration strategies at scale before deployment. The ideal candidate will have a solid understanding of robotics simulation and multiagent fundamentals along with the practical skills to deliver high-quality, maintainable code on a small, focused team. What You Will Get To Do Architect high-fidelity multi-robot simulation scenes (terrain, sensors, dynamic obstacles) and scale from single-agent tests to fleet scenarios. Integrate physics, perception, and comms models so agents behave realistically at fleet scale. Implement and evaluate fleet-level task allocation, coordination, and planning inside simulation. Build declarative mission tooling to run thousands of orchestration experiments and profile performance across latency, heterogeneity, and environment complexity. Calibrate simulation to real-world telemetry with field teams and reduce the sim-to-real gap. Build domain randomization, scenario generation, and automated regression/benchmark gates for fleet orchestration releases. Design logging, metrics, and visualization so every run yields actionable data and fleet KPIs (throughput, collisions, comms overhead, idle time). Maintain reproducible experiment infrastructure with versioned scenes, configs, and results. Partner with perception, planning, learning, DevOps, and infrastructure teams to run large-scale simulation and raise standards for fidelity, coverage, and release quality. What You Bring MS or PhD in Computer Science, Robotics, Aerospace, or a related field (or equivalent industry experience). 7+ years hands-on experience building simulation environments for robotics or autonomous systems. Proficiency in Python and C++; comfort reading and extending large codebases. Working knowledge of robotics simulation engines (e.g., MuJoCo, NVIDIA Isaac Sim, Genesis). Experience with multi-robot or multi-agent systems: task allocation, distributed planning, swarm coordination, or fleet management. Familiarity with standard robotics middleware. Strong problem-solving skills and ability to thrive in fast-paced, interdisciplinary teams. Ability to translate research into practical, field-deployable systems. What Will Set You Apart Publications or open-source contributions in multiagent systems, multi-robot planning, or fleet coordination (ICRA, RSS, IROS, AAMAS, CoRL). Experience with domain randomization, synthetic data generation, or sim-to-real transfer at scale. Background in distributed systems or cloud-based simulation orchestration. Hands-on work with real robot fleets in field environments (e.g., construction, mining, logistics, defense).Contributions to open-source robotics or simulation frameworks. Why Join FieldAI? FieldAI is tackling one of robotics’ hardest problems: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models™ advance perception, planning, localization, and manipulation with an emphasis on explainability and safety, so our systems can be trusted where it matters most. You will work alongside a world-class team that values creativity, resilience, and bold thinking. We bring a decade-long track record of real-world deployments, strong performance in DARPA challenges, and experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX. Our R&D organization is growing and anchored in Boston, with close collaboration across our teams in Southern California and with colleagues around the US and globally. Be Part of the Next Robotics Revolution Solving problems at this scale takes a team as unique as the mission. We are looking for people who push beyond conventional approaches, enjoy tackling tough and ambiguous questions, and bring interdisciplinary perspective. Our success depends on exceptional AI researchers and engineers, as well as strong software developers, product designers, field deployment experts, and communicators who can turn breakthroughs into real capability. We are headquartered in Irvine, Southern California, with teammates across the US and around the world. Join us to shape the future of embodied intelligence as part of a fun, close-knit team building systems that work in the real world. Equal Opportunity FieldAI celebrates diversity and is committed to creating an inclusive environment for all employees. Candidates and employees are evaluated based on merit, qualifications, and performance. We do not discriminate on the basis of race, color, religion, sex, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected status.

Requirements

Must-haves

What You Will Get To Do
Architect high-fidelity multi-robot simulation scenes (terrain, sensors, dynamic obstacles) and scale from single-agent tests to fleet scenarios.
Integrate physics, perception, and comms models so agents behave realistically at fleet scale.
Implement and evaluate fleet-level task allocation, coordination, and planning inside simulation.
Build declarative mission tooling to run thousands of orchestration experiments and profile performance across latency, heterogeneity, and environment complexity.
Calibrate simulation to real-world telemetry with field teams and reduce the sim-to-real gap.
Build domain randomization, scenario generation, and automated regression/benchmark gates for fleet orchestration releases.
Design logging, metrics, and visualization so every run yields actionable data and fleet KPIs (throughput, collisions, comms overhead, idle time).
Maintain reproducible experiment infrastructure with versioned scenes, configs, and results.
Partner with perception, planning, learning, DevOps, and infrastructure teams to run large-scale simulation and raise standards for fidelity, coverage, and release quality.
MS or PhD in Computer Science, Robotics, Aerospace, or a related field (or equivalent industry experience).
7+ years hands-on experience building simulation environments for robotics or autonomous systems.
Proficiency in Python and C++; comfort reading and extending large codebases.
Working knowledge of robotics simulation engines (e.g., MuJoCo, NVIDIA Isaac Sim, Genesis).
Experience with multi-robot or multi-agent systems: task allocation, distributed planning, swarm coordination, or fleet management.
Familiarity with standard robotics middleware.
Strong problem-solving skills and ability to thrive in fast-paced, interdisciplinary teams.
Ability to translate research into practical, field-deployable systems.
What Will Set You Apart
Publications or open-source contributions in multiagent systems, multi-robot planning, or fleet coordination (ICRA, RSS, IROS, AAMAS, CoRL).
Experience with domain randomization, synthetic data generation, or sim-to-real transfer at scale.
Background in distributed systems or cloud-based simulation orchestration.
Hands-on work with real robot fleets in field environments (e.g., construction, mining, logistics, defense).Contributions to open-source robotics or simulation frameworks.
Why Join FieldAI?

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipython

Platform: lever
Effort: quick