Swarm Engineer - Multi-Agent Task Planning

Swarm Engineer – Multi-Agent Task Planning

Location: Phoenix, AZ
Company Stage of Funding: Early-Stage Autonomous Robotics & Defense Technology Startup
Office Type: On-site
Salary: $150,000 - $160,000+ Equity

Company Description

We’re representing an early-stage autonomous robotics company building low-cost swarm systems for defense and industrial applications. Their platform combines autonomous navigation, distributed coordination, edge AI, and large-scale swarm planning to enable fleets of autonomous ground vehicles to operate collaboratively in dynamic environments.

The company is focused on building scalable, resilient robotic systems that can coordinate complex behaviors across large groups of autonomous agents. Their engineering team includes experienced operators and engineers from leading robotics, autonomous systems, aerospace, and defense organizations. This role will directly influence the intelligence and coordination systems powering next-generation autonomous swarm capabilities.

What You Will Do

• Design, train, and deploy multi-modal action models that enable coordinated swarm-level behaviors across autonomous robotic systems
• Develop reinforcement learning and transformer-based architectures for tactical decision-making and macro-action selection
• Build models that fuse heterogeneous inputs including local perception, swarm state, mission objectives, and environmental context
• Train and optimize online and offline reinforcement learning systems for multi-agent coordination and planning
• Deploy models to resource-constrained edge hardware with a focus on low-latency real-time execution
• Build and maintain the full ML lifecycle including data collection, curation, training, evaluation, and deployment
• Integrate learned action models into broader autonomy stacks alongside navigation, planning, and swarm coordination systems
• Conduct field validation and deployment testing on physical autonomous robotic platforms
• Collaborate closely with autonomy, systems, infrastructure, and robotics engineering teams to improve system-wide performance

Ideal Background

• Strong background in machine learning, reinforcement learning, and multi-agent systems
• Experience building models that output actions, policies, or macro-actions rather than purely perception or classification systems
• Deep understanding of neural networks, transformers, sequence modeling, and statistical learning techniques
• Strong programming skills in Python and C++
• Experience deploying machine learning systems into real-world production or edge environments
• Familiarity with distributed coordination systems, swarm intelligence, task allocation, or autonomous planning systems
• Ability to work in ambiguous, fast-moving engineering environments with significant ownership and autonomy
• Strong systems thinking with the ability to reason across robotics, infrastructure, ML, and distributed systems

Preferred

• Hands-on reinforcement learning experience with PPO or related policy optimization techniques
• Experience with multi-agent task planning, scheduling systems, auction-based coordination, or distributed optimization
• Familiarity with ONNX, TensorRT, or model optimization techniques for edge deployment
• Background in robotics, autonomous vehicles, UAVs, or unmanned systems
• Experience with simulation environments and synthetic data generation for training autonomous systems
• End-to-end ownership of ML systems from experimentation through production deployment
• Publications or research experience in machine learning, robotics, reinforcement learning, or autonomous systems
• Familiarity with modern robotics frameworks or distributed autonomy stacks

Compensation and Benefits

• Competitive salary + meaningful equity package
• Opportunity to work on cutting-edge multi-agent autonomy and reinforcement learning systems
• High ownership role with direct impact on core autonomous swarm capabilities
• Work alongside senior robotics, autonomy, and defense technology engineers
• Fast-moving startup environment with significant technical influence and greenfield engineering opportunities
• Opportunity to deploy real-world autonomous systems operating in challenging environments