Senior Manager, Engineering - Hivemind Reasoning (R6291)

Shield AI•San Mateo, CA

About The Position

Shield AI is seeking a leader to build reasoning models for unmanned autonomous systems across aerial, maritime, land, and space domains. The role leads a team that researches, designs, trains, adapts, and evaluates models intended for deployment on unmanned platforms. The models must support both individual vehicles and coordinated teams of hundreds of agents, while meeting the operational constraints of edge deployment in contested environments. The successful candidate will define the technical roadmap for single-agent and multi-agent models, guide architecture and training choices, and establish data strategies and evaluation criteria. The role involves balancing foundation-model approaches, specialized deep-learning models, and traditional AI/ML techniques; advancing models’ abilities to interpret context, reason over time, coordinate agents, and adapt to changing or incomplete information; and creating rigorous simulation and real-world evaluations of generalization, robustness, uncertainty, and performance. The Senior Manager will recruit, develop, and manage a high-performing AI/ML team; work with perception, MLOps, simulation, inference, hardware, and autonomy teams; communicate technical tradeoffs and limitations to technical and organizational stakeholders; and deliver trained models with clear performance evidence to partner teams. Success means delivering reasoning models that perform exceptionally on baseline platforms and missions and can be confidently adapted to new platforms and missions.

Requirements

Bachelor’s or master’s degree in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or a similar field, or equivalent practical experience.
Typically at least 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 leading development and production deployment of state-of-the-art ML models, making prioritization decisions, and communicating tradeoffs across disciplines.
At least 5 years of people-management experience leading engineering or applied research teams, including 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, including self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.
Experience defining datasets, training objectives, experimental workflows, benchmarks, and reproducible model-comparison criteria.
Fluency in traditional AI/ML techniques and ability to combine them thoughtfully with learned models.
Experience with training frameworks such as PyTorch, JAX, or TensorFlow.
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, such as path planning, probabilistic modeling, and optimization approaches.
Familiarity with NVIDIA Jetson and other edge compute platforms, such as Qualcomm or AMD.

Nice To Haves

PhD in Computer Science, Electrical Engineering, or a similar field.
Expertise optimizing 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, including rigid-body dynamics and sensor and actuator models.
Experience managing and interfacing with external partners and vendors.

Responsibilities

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 architectures, training methods, data strategy, and evaluation criteria.
Make informed tradeoffs among foundation-model approaches, specialized deep-learning models, and traditional AI/ML methods.
Advance model capabilities for interpreting context, reasoning over time, coordinating agents, and adapting to incomplete or changing information.
Drive training efforts using appropriate approaches such as self-supervised learning, imitation learning, reinforcement learning, and distillation.
Establish rigorous evaluations using simulation and 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 to research, engineering, product, and organizational leadership.
Deliver trained models and clear performance evidence to partner teams.
Create feedback loops connecting model failures, data curation, training experiments, and re-evaluation.
Partner with teams in perception, MLOps, large-scale simulation, inference, and the broader Hivemind Enterprise team.