Engineer

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. 

Job Description:

Help shape the future of physical AI in defense by building reasoning models for unmanned autonomous systems across aerial, maritime, land, and space domains. 

We’re looking for a deeply technical engineer who can turn emerging research into reliable autonomous capabilities and shares our conviction that AI can protect service members and civilians. As a Staff Engineer on the Hivemind Reasoning team, you’ll drive the design, training, adaptation, and evaluation of reasoning and cognition models intended for deployment on unmanned platforms. 

You’ll take hands-on ownership of complex technical problems, from defining model architectures and training objectives to establishing rigorous evidence of mission effectiveness. Your work will support individual vehicles and coordinated teams of hundreds of agents, while accounting for limited compute, intermittent communications, incomplete information, and other constraints of operating at the edge in contested environments. 

You’ll partner closely with teams in perception, MLOps, large-scale simulation, autonomy, and inference to deliver capabilities as part of Hivemind Enterprise.  

What you'll do:

• Design, implement, train, adapt, and evaluate reasoning and cognition models for unmanned autonomous platforms. 
• Make and validate tradeoffs among foundation-model approaches, including vision-language models (VLMs) and vision-language-action models (VLAs), specialized deep-learning models, and traditional AI/ML methods. 
• Develop capabilities for interpreting context, reasoning over time, coordinating among agents, and adapting to incomplete or changing information. 
• Build reproducible training and adaptation workflows using appropriate combinations of self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.