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. Europe, the Middle East, and Asia-Pacific, Shield AI's technology actively supports operations worldwide. 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. 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. 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. 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. 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. 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; 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. Experience with training frameworks such as PyTorch, JAX, TensorFlow, etc. Strong familiarity with traditional robotics algorithms, e.g. path planning, probabilistic modeling, optimization approaches, etc. PhD in Computer Science, Electrical Engineering, or similar degree. Experience managing managers or leading multiple engineering teams. Full-time regular employee offer package: Temporary employee offer package: Pay within range listed above + temporary benefits package (applicable after 60 days of employment) If you have a disability or special need that requires accommodation, please let us know. If you would like more information about how your data is processed, please contact us.

📌 Senior Manager, Engineering (España)
🏢 shieldai
📍 España