Founded in 2015, 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 and V-BAT and X-BAT aircraft. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit
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Job Description:
The Product Manager will drive the strategy and execution of Shield AI’s next-generation autonomy intelligence stack—enabling customers and internal teams to train, evaluate, and deploy foundation and domain models that power resilient autonomy at the edge. This PM owns the product vision and roadmap for the Hivemind AI Platform (Forge, training pipelines, data infrastructure, evaluation, and deployment toolchains), ensuring we can manufacture, govern, and field advanced world models, robotics foundation models, and vision-language-action systems safely and at scale.
This role sits at the intersection of AI/ML, autonomy, model lifecycle, infrastructure, and product strategy. The PM partners closely with engineering, AI research, Hivemind Solutions, and field teams to deliver the tooling that enables sovereign autonomy, AI Factories at the edge, and continuous learning—capabilities that are central to Shield AI’s strategic direction.
This is a high-impact role for an experienced product leader excited to define how foundation models are trained, validated, governed, and deployed across thousands of autonomous systems in highly contested environments.
What you'll do:
AI Model Development & Training Platform
Own the roadmap for foundation model training workflows, including dataset ingestion, curation, labeling, synthetic data generation, domain model training, and distillation pipelines.
Define requirements for world models, robotics models, and VLA-based training, evaluation, and specialization.
Lead the evolution of MLOps capabilities in Forge, including data lineage, experiment tracking, model versioning, and scalable evaluation suites.
Data, Simulation & Synthetic Data Factory
Define product requirements for synthetic data generation, simulation-integrated data flywheels, and automated scenario generation.
Partner with Digital Twin, Simulation, and autonomy teams to convert natural-language mission inputs into data needs, training procedures, and model variants.
Safe Deployment & Model Governance
Lead the development of model governance and auditability tooling, including model cards, dataset rights, lineage tracking, safety gates, and compliance evidence.
Build guardrails and workflows to safely deploy models onto edge hardware in disconnected, GPS- or comms-denied environments.
Partner with Safety, Certification, Cyber, and Engineering teams to ensure traceability and evaluation pipelines meet operational and accreditation requirements.
Edge Deployment & AI Factory Integration
Partner with Pilot, EdgeOS, and hardware teams to integrate foundation-model-based perception and reasoning into autonomy behaviors.
Define requirements for distillation, quantization, and inference tooling as part of the “three-computer” development and deployment model.
Ensure closed-loop workflows between cloud model training and edge-native execution.
Cross-Functional Leadership
Collaborate with Engineering, Research, Product, Customer Engagement, and Solutions teams to ensure model outputs meet mission and platform constraints.
Translate advanced AI capabilities into intuitive workflows that platform OEMs and partner nations can use to build sovereign AI factories.
Sequence foundational capabilities that unblock autonomy, simulation, and customer-facing product teams.
User & Customer Impact
Develop deep empathy for ML engineers, autonomy developers, and Solutions engineers who rely on the platform.
Capture operational data gaps, mission-driven model needs, and domain-specific specialization requirements.
Lead demos and onboarding for model-development capabilities across internal and external teams.
Required qualifications:
7+ years of experience in product management or highly technical ML/AI product roles.
2+ years of experience in a hands-on software development role.
Strong engineering background (Computer Science, Electrical Engineering, Robotics, or related field).
Deep understanding of foundation models, robotics models, multimodal models, MLOps, and training infrastructure.
Experience managing complex products spanning data pipelines, cloud training clusters, model governance, and edge deployments.
Proven success partnering with research teams to transition ML innovations into stable, production-grade workflows.
Familiarity with simulation-based data generation and large-scale data management.
Excellent communicator with strong cross-functional leadership skills.
Preferred qualifications:
Experience working on autonomy, robotics, embedded AI, or mission-critical systems.
Hands-on familiarity with GPU infrastructure, distributed training, or data lakehouse architectures.
Experience supporting defense, dual-use, or safety-critical AI systems.
Background designing or operating AI Factory–style pipelines (data → training → evaluation → distillation → edge deployment).
Advanced degree in engineering, ML/AI, robotics, or a related field.
