Swarm Engineer - Multi-Agent Task Planning

Company background Swarmbotics AI is a low-cost, swarm robotics company for industry and defense. We see a world of ubiquitous low-cost robots transforming almost all aspects of society, but we see an urgent need in the defense industry. We focus on building swarms of robots that incorporate a low-cost BOM, an autonomous stack optimized for off the shelf components, and a global planner that enables swarm capabilities for groups of robots to accomplish sophisticated tasks. Our first product is a defense application building Unmanned Ground Vehicles (UGVs), collectively termed - Attritable, Networked, Tactical Swarm (ANTS). Each UGV in ANTS is an independently-tasked, attritable robot designed for on-demand and autonomous mobility. When operating as a swarm, ANTS is capable of executing more advanced and coordinated, high-level capabilities across a battlespace. ANTS will help solve some of the DoD’s biggest problems that will save lives and increase defense capabilities. Stephen Houghton and Drew Watson are the Founders and have decades of experience in self-driving cars and trucks, humanoids, and UAVs with experience from NASA, JPL, Cruise, Embark, McKinsey, Amazon, and the CIA. Job description Swarmbotics AI is seeking a Machine Learning Engineer to design, develop, and deploy a multi-modal action model that enables each UGV to select and execute coordinated swarm macro-actions in real time. This role sits at the intersection of machine learning and multi-agent decision making: you will build learned models that reason over multi-modal inputs to perform tactical macro-actions. This is not a perception role. The core focus is on the decision-making and action-selection layers — training models that translate situational awareness into intelligent swarm behavior. You will work closely with company leadership and cross-functional teams to align capabilities with the Swarmbotics AI product roadmap. What You'll Do Architect, train, and iterate on multi-modal action models that select swarm-level tactical macro-actions from rich contextual inputs Design model architectures that fuse heterogeneous inputs — local perception, swarm state, mission objectives — into a unified decision representation Develop and apply online and offline reinforcement learning approaches, including transformer-based sequence modeling, to learn swarm coordination policies Optimize models to run real-time on edge devices through quantization, distillation, and efficient architecture design Build and maintain the full pipeline from data collection and curation through training, evaluation, and field deployment Integrate the action model into the broader autonomy stack alongside navigation, planning, and swarm coordination subsystems Deploy and validate trained models on physical UGV swarms in field environments Write robust Python and C++ code Required qualifications Strong mathematical foundation in neural networks, transformers, reinforcement learning, and statistics Proficiency in Python and C++ Experience with PyTorch or TensorFlow Experience training and deploying models that produce actions or macro-actions (e.g., online or offline reinforcement learning, planning-as-inference, VLA models, or similar) — not solely classification or perception Familiarity with multi-agent coordination concepts: task allocation, distributed decision-making, or swarm behaviors Experience optimizing and deploying ML models on resource-constrained or edge hardware Preferred qualifications Hands-on experience with policy gradient methods such as PPO Experience with multi-agent task planning algorithms (task allocation, scheduling, auction-based methods) Familiarity with ONNX, TensorRT, and edge deployment toolchains Prior robotics experience, autonomous driving background, or work with unmanned systems Experience with simulation environments and synthetic data generation for training multi-agent policies Experience owning an entire data-to-production model pipeline Academic publications in related fields (e.g., NeurIPS, AAAI, IROS, ICRA, JAIR) Experience with a CatBs framework is preferred but not required The preceding description is not designed to be a complete list of all duties and responsibilities required for the position. Swarmbotics is an equal‑opportunity employer. All qualified applicants will be treated with respect and receive equal consideration for employment without regard to race, color, caste, creed, religion, sex, gender identity, sexual orientation, national origin, ancestry, disability, uniform service, Veteran status, age, or any other protected characteristic per federal, state, or local law. #J-18808-Ljbffr Swarmbotics AI