Autonomy Engineer, Applied Intuition

• Bachelor’s degree in computer science/related engineering field
• 3+ years of hands-on experience developing multi-agent teaming software for autonomous robotic systems
• Experience with C++/Python Autonomy algorithms
• Experience developing decision making AI for autonomous unmanned systems
• Strong procedural and object-oriented programming experience that employs clean code principles and good OOP design patterns/principles
• Experience developing in Docker and containerized development environments, using Linux-based operating systems (e.g., RHEL, Ubuntu), source control systems, and artifact registries
• Must be a U.S. Citizen
• Must hold or be eligible to obtain and maintain a U.S. security clearance

Desirable

• As a Multi-Agent Teaming Autonomy Engineer on the Air Combat Autonomy team at EpiSci, your role is pivotal
• You'll be designing, implementing, and optimizing algorithms for groups of unmanned autonomous systems​ that are operating in the larger context of human-machine teams in a contested and uncertain battlefield environment
• Success in this role will require the ability to rapidly prototype and iterate, use data-driven workflows to find the best solutions, collaborate with high-performing teammates, and employ a combination of proven techniques and creative new approaches
• Build decision making architectures for coordination of actions across teams of homogeneous or heterogeneous autonomous agents in real world environments with limited human interaction
• Develop and implement techniques to promote explainability within the decision-making architecture
• Write software that operates real autonomous aircraft systems including F16 fighter jets, group 1-5 unmanned aerial vehicles (UAVs), as well as simulated models to accomplish tactical military missions
• Collaborate with DoD warfighter domain experts and 3rd party UAS vehicle vendors to develop and integrate autonomy software onto OEM hardware
• Implement and optimize autonomy algorithms for tasks such as sensor processing, track propagation and correlation, environment mapping, mission and trajectory planning, reasoning over discrete or uncertain states, optimization, learning, and control theory