Senior Manager Engineering Hivemind Reasoning
Shield AI is a U.S. defense-technology company developing mission-autonomy software and autonomous aircraft for military and allied operations.
Funding history
Investors
About Shield AI
Founded in 2015, Shield AI builds Hivemind autonomy software and V-BAT and X-BAT aircraft for operations in contested, GPS- and communications-denied environments. Its current site also presents Aechelon synthetic-reality simulation and Vision Systems detection and tracking products.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build and lead an AI and machine-learning engineering team developing reasoning and cognition models for unmanned platforms. You will define technical roadmaps, architectures, training approaches, data strategy, and evaluation criteria. You will guide model deployment, evaluate performance, manage engineering staff, and communicate technical tradeoffs to stakeholders.
Requirements
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or similar discipline, or equivalent practical experience
- 10 years of related experience with a Bachelor's degree, 9 years with a Master's degree, or 8 years with a PhD
- 5+ years of people-management experience
- Foundation models
- Deep learning
- Model architecture
- Self-supervised learning
- Supervised fine-tuning
- Imitation learning
- Reinforcement learning
- Distillation
- Dataset definition
- Experimental workflows
- Benchmarking
- Traditional AI and machine learning
- PyTorch
- JAX
- TensorFlow
- Multimodal models
- Robotics
- Autonomous systems
- Path planning
- Probabilistic modeling
- Optimization
- NVIDIA Jetson
- Edge computing
Responsibilities
- Build and lead a team designing, training, adapting, and evaluating reasoning and cognition models
- Define technical roadmaps for single-agent and multi-agent models
- Select model architectures, training methods, data strategy, and evaluation criteria
- Advance model capabilities for contextual interpretation, reasoning, coordination, and adaptation
- Drive self-supervised learning, imitation learning, reinforcement learning, and distillation efforts
- Establish model evaluations using simulation and real-world data
- Recruit, develop, and manage AI and machine-learning engineers
- Translate edge constraints into model-design and training decisions
- Communicate technical tradeoffs and model limitations
- Deliver trained models and performance evidence to partner teams
- Create feedback loops among model failures, data curation, training experiments, and re-evaluation
Benefits
- Bonus
- Benefits
- Equity