Product Manager, AI Platforms (R4991)

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.