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.
Define data requirements, training objectives, benchmarks, and evaluation methods that support meaningful comparisons among model approaches.
Establish rigorous evaluations in simulation and with real-world data, measuring generalization, robustness, uncertainty, and performance as team size and operating conditions change.
Incorporate edge constraints into model architecture and training decisions in partnership with hardware, autonomy, and inference teams.
Diagnose model failures and create feedback loops among data curation, training experiments, and re-evaluation.
Deliver trained models, reproducible results, and clear performance evidence to partner teams.
Communicate technical tradeoffs, experimental findings, and model limitations clearly to research, engineering, product, and organizational leadership.
Required qualifications:
BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience. Typically requires a minimum of 7 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or 4 years with a PhD; or equivalent work experience.
A demonstrated track record of developing and deploying advanced ML models in production environments, with substantial hands-on contributions to model design, training, and evaluation.
Deep expertise in foundation models, modern deep learning, model architectures, and the training ecosystem.
Strong command of training and adaptation approaches such as self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.
Experience defining datasets, training objectives, experimental workflows, benchmarks, and reproducible criteria for comparing models.
Fluency in traditional AI/ML techniques and an ability to combine them thoughtfully with learned models.
Experience with training frameworks such as PyTorch, JAX, TensorFlow, etc.
Experience with multimodal, embodied, robotic, or autonomous-system models; familiarity with VLA models and world models is particularly relevant.
Familiarity with traditional robotics algorithms, e.g. path planning, probabilistic modeling, optimization approaches, etc.
Familiarity with NVIDIA Jetson and other edge compute platforms (e.g. Qualcomm, AMD, etc.).
Preferred qualifications:
MS or PhD in Computer Science, Electrical Engineering, or similar degree.
Expertise in optimising and deploying ML models to resource-constrained hardware platforms.
Experience with simulation-to-real on autonomous systems.
Experience with unmanned autonomous systems in defense applications.
Familiarity with large, distributed systems.
Familiarity with high-fidelity simulation, e.g. rigid body dynamics, sensor & actuator models, etc.
