Swarm Engineer - Multi-Agent Task Planning

Recruiting From Scratch • Phoenix, AZ

2d • $150,000 - $160,000 • Onsite

About The Position

As a Swarm Engineer – Multi-Agent Task Planning , you will design and deploy multi-modal action models that enable real-time coordinated swarm behaviors. This is not a perception role — the focus is on decision-making and action selection at both individual vehicle and swarm levels. This role operates at the core of swarm intelligence — translating situational awareness into coordinated, tactical action.

Requirements

• 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 generate actions or macro-actions (e.g., reinforcement learning, planning-as-inference, VLA models).
• Familiarity with multi-agent coordination concepts such as task allocation, distributed decision-making, or swarm behaviors.
• Experience optimizing and deploying ML models on resource-constrained or edge hardware.
• Eligible to work on export-controlled projects and able to relocate to Phoenix, AZ.

Nice To Haves

• Experience with policy gradient methods (e.g., PPO).
• Experience with multi-agent task planning algorithms (auction-based allocation, distributed scheduling, swarm coordination).
• Familiarity with ONNX, TensorRT, and edge deployment toolchains.
• Prior experience in robotics, autonomous vehicles, or unmanned systems.
• Experience with simulation environments and synthetic data generation for training multi-agent policies.
• Experience owning an end-to-end data-to-production model pipeline.
• Academic publications in related fields (NeurIPS, AAAI, IROS, ICRA, JAIR, etc.).

Responsibilities

• Architect, train, and iterate on multi-modal action models that select tactical macro-actions based on rich contextual inputs.
• Design model architectures that fuse heterogeneous data sources (local perception outputs, swarm state, mission objectives) into unified decision representations.
• Develop and apply reinforcement learning approaches (online and offline), including transformer-based sequence modeling for swarm coordination.
• Optimize models for real-time edge execution using quantization, distillation, and efficient architecture design.
• Build and maintain full ML pipelines from data collection and curation through training, evaluation, and field deployment.
• Integrate action models into broader autonomy stacks alongside navigation and planning systems.
• Deploy and validate trained policies on physical UGV swarms in field environments.
• Write robust production-quality Python and C++ code.

Benefits

• Competitive salary based on experience.
• Full-time, onsite in Phoenix, AZ.
• Direct ownership of swarm-level intelligence systems in a defense robotics platform.
• Opportunity to define and scale multi-agent action architectures from the ground up.
• This role is ideal for engineers motivated by applying machine learning to real-world, high-impact autonomous systems in defense contexts.
• The company is committed to equal employment opportunity and complies with all applicable federal, state, and local employment laws.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed