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Shield AI is seeking a Senior Manager of Engineering to lead the Hivemind Reasoning team, responsible for designing, training, adapting, and evaluating reasoning and cognition models for deployment across unmanned autonomous platforms in defense applications.
You will define the technical roadmap for single-agent and multi-agent AI/ML models, making informed architectural and methodological tradeoffs among foundation models (VLMs, VLAs), specialized deep-learning approaches, and traditional AI/ML techniques. Key responsibilities include advancing model capabilities for context interpretation, temporal reasoning, multi-agent coordination, and adaptation to incomplete or changing information. You will drive training efforts leveraging self-supervised learning, imitation learning, reinforcement learning, and distillation techniques.
You will establish rigorous evaluation frameworks in simulation and with real-world data, measuring generalization, robustness, uncertainty quantification, and performance across varying team sizes and operating conditions. You will recruit, develop, and manage a high-performing team of AI/ML engineers, translating edge-compute constraints into model-design decisions in partnership with hardware, autonomy, and inference teams. Communication of technical tradeoffs and model limitations to research, engineering, product, and leadership is critical. You will deliver trained models with clear performance evidence and create tight feedback loops between model failures, data curation, training experiments, and re-evaluation.
REQUIREMENTS:
- BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or equivalent practical experience. Typically 10+ years with Bachelor's; 9+ years with Master's; 8+ years with PhD; or equivalent work experience.
- Exceptional track record leading development and deployment of state-of-the-art ML models in production, making difficult prioritization decisions, and communicating tradeoffs across disciplines.
- 5+ years of people management experience leading engineering or applied research teams, with demonstrated success in 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: self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.
- Experience defining datasets, training objectives, experimental workflows, benchmarks, and reproducible comparison criteria for models.
- Fluency in traditional AI/ML techniques and ability to combine them thoughtfully with learned models.
- Experience with training frameworks (PyTorch, JAX, TensorFlow, etc.).
- Experience with multimodal, embodied, robotic, or autonomous-system models; familiarity with VLA models and world models particularly relevant.
- Strong familiarity with traditional robotics algorithms (path planning, probabilistic modeling, optimization).
- Familiarity with NVIDIA Jetson and other edge compute platforms (Qualcomm, AMD, etc.).
PREFERRED:
- PhD in Computer Science, Electrical Engineering, or similar.
- Expertise optimizing and deploying ML models to resource-constrained hardware.
- 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 (rigid body dynamics, sensor/actuator models).
- Experience managing and interfacing with external partners and vendors.
