Senior Manager, Engineering - Hivemind Reasoning (R6291)

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:

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

We are looking for a leader with not only the deep technical expertise or the abilities to build a world-class team, but also someone with the strong conviction that AI will protect service members and civilians. This role is responsible for building and leading a team that researches and designs models, establishes training pipelines and rigorous evaluations, and ensures mission effectiveness at the edge under real-world constraints. These models must support individual vehicles and coordinated teams of hundreds of agents, while meeting the constraints of operating at the edge in contested environments.

In this role, you’ll partner closely with world-class teams in perception, MLOps, large-scale simulation, and inference to deliver capabilities as part of the broader Hivemind Enterprise team.

What success looks like: your team’s reasoning models deliver exceptional outcomes for baseline platforms and missions, which can then be rapidly and confidently adapted to new platforms and missions by our solutions engineering organisation.

What you'll do:

• Build & lead the team that designs, trains, adapts, and evaluates reasoning and cognition models intended for deployment across our unmanned platforms.
• Define the technical roadmap for single-agent and multi-agent models, including architecture choices, training methods, data strategy, and evaluation criteria.
• Make informed tradeoffs among foundation-model approaches (e.g. VLMs, VLAs), specialized deep-learning models, and traditional AI/ML methods.
• Advance model capabilities for interpreting context, reasoning over time, coordinating among agents, and adapting to incomplete or changing information.
• Drive training efforts, including appropriate use of self-supervised learning, imitation learning, reinforcement learning, and distillation.
• Establish rigorous evaluations in simulation and with real-world data. Measure generalization, robustness, uncertainty, and performance as team size and operating conditions change.
• Recruit, develop, and manage a high-performing team of AI/ML engineers.
• Translate edge constraints into model-design and training decisions in partnership with hardware, autonomy, and inference teams.
• Communicate technical tradeoffs and model limitations clearly to research, engineering, product, and organizational leadership.
• Deliver trained models and clear performance evidence to partner teams.
• Create tight 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, and/or similar degree, or equivalent practical experience. Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 8 years with a PhD; or equivalent work experience.
• Exceptional track record of leading the development and deployment of state-of-the-art ML models in production environments, making difficult prioritisation decisions, and communicating tradeoffs across disciplines.
• 5+ years of people management experience leading engineering or applied research teams, with demonstrated success hiring, coaching, developing technical leaders, and managing performance.
• 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.
• Strong 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:

• 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 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, e.g. rigid body dynamics, sensor & actuator models, etc.
• Experience managing and interfacing with external partners and vendors

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