Senior Manager, Engineering - Hivemind Reasoning (R6291)

San Mateo · USD 290,000-430,000 / year · 5+ years experience · Onsite · Posted Oct 15, 2026

At a glance

The Senior Manager, Engineering - Hivemind Reasoning will lead a team focused on designing, training, and adapting reasoning and cognition models for unmanned platforms.
This role involves defining technical roadmaps for single-agent and multi-agent models while balancing foundation-model approaches with traditional AI/ML methods.
The position requires driving training efforts and establishing rigorous evaluation frameworks for deployment in real-world and simulated environments.

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, and data strategy.
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, and coordinating among agents.
Drive training efforts including self-supervised learning, imitation learning, reinforcement learning, and distillation.
Establish rigorous evaluations in simulation and with real-world data to measure robustness and performance.
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 and autonomy teams.
Communicate technical tradeoffs and model limitations to research, engineering, and organizational leadership.
Create tight feedback loops among model failures, data curation, training experiments, and re-evaluation.

BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or equivalent practical experience.
Typically requires 10 years of experience with a Bachelor’s, 9 years with a Master’s, or 8 years with a PhD.
Exceptional track record of leading the development and deployment of state-of-the-art ML models in production environments.
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 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, and benchmarks.
Fluency in traditional AI/ML techniques and ability to combine them with learned models.
Experience with training frameworks such as PyTorch, JAX, or TensorFlow.
Experience with multimodal, embodied, robotic, or autonomous-system models.

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.
Experience managing and interfacing with external partners and vendors.