Senior Manager, Engineering - Hivemind Reasoning (R6291)

The role

Lead and build a team that designs, trains and evaluates reasoning and cognition models for unmanned autonomous systems across aerial, maritime, land and space domains. Define the technical roadmap, drive training and evaluation, and hire and manage AI/ML engineers.

What you will do

• Build and lead the team that designs, trains, adapts and evaluates reasoning and cognition models for unmanned platforms.
• Define the technical roadmap for single-agent and multi-agent models, including architecture, training methods, data strategy and evaluation criteria.
• Make tradeoffs among foundation-model approaches, specialized deep-learning models and traditional AI/ML methods.
• Advance model capabilities for interpreting context, reasoning over time, coordinating among agents and adapting to changing information.
• Drive training efforts using self-supervised learning, imitation learning, reinforcement learning and distillation.
• Establish rigorous evaluations in simulation and with real-world data.
• Recruit, develop and manage a high-performing team of AI/ML engineers.
• Translate edge constraints into model-design and training decisions with hardware, autonomy and inference teams.
• Communicate technical tradeoffs and model limitations to research, engineering, product and organizational leadership.
• Create feedback loops among model failures, data curation, training experiments and re-evaluation.

Required qualifications

• BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering or a similar degree, or equivalent practical experience.
• Minimum 10 years of related experience with a Bachelor's degree; 9 years with a Master's degree; or 8 years with a PhD; or equivalent work experience.
• Exceptional track record of leading development and deployment of state-of-the-art ML models in production.
• 5+ years of people management experience leading engineering or applied research teams.
• 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.
• Strong 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

• PhD in Computer Science, Electrical Engineering or a similar degree.
• Expertise optimizing and deploying ML models to resource-constrained hardware.
• Experience with simulation-to-real on autonomous systems.
• Experience managing managers or leading multiple engineering teams.
• Experience with unmanned autonomous systems in defense applications.
• Familiarity with large, distributed systems.
• Familiarity with high-fidelity simulation.
• Experience managing and interfacing with external partners and vendors.

Benefits

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

Before you apply

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

About the company

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