Senior Manager, Engineering - Hivemind Reasoning (R6291)

Shield AI•San Mateo, CA

2d•$290,000 - $430,000

About The Position

Shield AI is hiring a Senior Manager, Engineering to lead the development of reasoning and cognition models for unmanned autonomous systems across aerial, maritime, land, and space domains. The role combines technical leadership with team building and is responsible for models that support individual vehicles as well as coordinated teams of hundreds of agents, while operating at the edge in contested environments. The successful candidate will lead a team that researches, designs, trains, adapts, and evaluates models, establishes training pipelines and rigorous evaluations, and demonstrates mission effectiveness under real-world constraints. The role defines technical direction for single-agent and multi-agent models, makes informed tradeoffs among foundation models, specialized deep-learning models, and traditional AI/ML methods, and partners with perception, MLOps, simulation, inference, hardware, autonomy, and solutions engineering teams. The team’s models should deliver strong results for baseline platforms and missions and be adaptable 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, 8 years with a PhD, or equivalent work experience.
Exceptional track record leading the 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 a 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 technical 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 approaches such as self-supervised learning, imitation learning, reinforcement learning, and distillation.
Establish rigorous evaluations in simulation and with real-world data, measuring 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-computing 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.