Senior Manager, Engineering - Hivemind Reasoning (R6291)

About the role

Responsibilities

• 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.

Requirements

• 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.)
• 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