Staff Engineer, Hivemind Reasoning (R6292)

The role

You will design, train, adapt and evaluate reasoning and cognition models for unmanned autonomous platforms across aerial, maritime, land and space domains. You will work with teams in perception, MLOps, simulation, autonomy and inference to deliver capabilities as part of Hivemind Enterprise.

What you will do

• Design, implement, train, adapt and evaluate reasoning and cognition models for unmanned autonomous platforms.
• Make and validate tradeoffs among foundation-model approaches, including VLMs, 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 self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning and distillation.
• Define data requirements, training objectives, benchmarks and evaluation methods.
• Establish rigorous evaluations in simulation and with real-world data.
• Incorporate edge constraints into model architecture and training decisions 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 to research, engineering, product and leadership.

Required qualifications

• BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering or similar, or equivalent practical experience.
• Minimum of 7 years of related experience with a Bachelor's degree; or 6 years with a Master's degree; or 4 years with a PhD; or equivalent work experience.
• Track record of developing and deploying advanced ML models in production environments.
• Deep expertise in foundation models, modern deep learning, model architectures and the training ecosystem.
• Strong command of self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning and distillation.
• Experience defining datasets, training objectives, benchmarks and reproducible criteria for comparing models.
• Fluency in traditional AI/ML techniques.
• Experience with PyTorch, JAX, TensorFlow or similar training frameworks.
• Experience with multimodal, embodied, robotic or autonomous-system models.
• Familiarity with traditional robotics algorithms such as path planning, probabilistic modeling and optimization.
• Familiarity with NVIDIA Jetson and other edge compute platforms.

Nice to have

• MS or PhD in Computer Science, Electrical Engineering or similar.
• Expertise in optimizing and deploying ML models to resource-constrained hardware.
• 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, such as rigid body dynamics and sensor and actuator models.

Benefits

• Bonus
• Equity (full-time regular employees)
• Benefits package (full-time regular employees)
• Temporary benefits package (temporary employees, applicable after 60 days of employment)

Before you apply

• All offers are contingent on a cleared background and possible reference check.
• Pay depends on factors including skill set, experience, licenses and certifications, and work location.
• Military fellows and part-time employees are not eligible for benefits.

About the company

Shield AI is a venture-backed defense-tech company that protects service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation technologies.