Product Manager, AI/ML and Foundation Models

Shield AI; San Diego (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Shield AI is hiring a product manager for AI/ML and foundation models inside its Hivemind autonomy platform. The role owns the workflows and product requirements around training pipelines, data infrastructure, synthetic data, model evaluation, governance, and deployment onto edge autonomy systems.

This is a deeply technical product role. The user is likely an autonomy engineer, ML engineer, solutions engineer, or partner building and validating models for operational systems.

Qualifications

• Product management experience for technical ML, AI, or data infrastructure products.
• Hands-on software or engineering background.
• Understanding of foundation models, robotics models, MLOps, and edge deployment.

Responsibilities

• Define the roadmap for foundation-model training workflows, dataset pipelines, and model evaluation.
• Shape requirements for world models, robotics models, VLA training, and model lifecycle tooling.
• Build governance workflows for model cards, lineage, safety gates, and deployment evidence.
• Partner with autonomy, simulation, hardware, research, safety, and field teams.

Skills

Product management

ML/AI domain

Data infrastructure

Tools

Software engineering

MLOps

Job description

What the role actually is

Shield AI is hiring a product manager for AI/ML and foundation models inside its Hivemind autonomy platform. The role owns the workflows and product requirements around training pipelines, data infrastructure, synthetic data, model evaluation, governance, and deployment onto edge autonomy systems.

This is a deeply technical product role. The user is likely an autonomy engineer, ML engineer, solutions engineer, or partner building and validating models for operational systems.

What you would work on

• Define the roadmap for foundation-model training workflows, dataset pipelines, and model evaluation
• Shape requirements for world models, robotics models, VLA training, and model lifecycle tooling
• Build governance workflows for model cards, lineage, safety gates, and deployment evidence
• Partner with autonomy, simulation, hardware, research, safety, and field teams

What they are asking for

• Product management experience for technical ML, AI, or data infrastructure products
• Hands-on software or engineering background
• Understanding of foundation models, robotics models, MLOps, and edge deployment
• Ability to work across research, product, customer, and mission-critical engineering teams

Why this one is worth a look

As robotics moves toward foundation-model-based autonomy, the product surface is no longer just an operator interface. It is also the internal machinery for training, evaluating, governing, and safely deploying models. This role is interesting because it treats that machinery as a product in its own right.