# Product Manager, AI Platforms (R4991)

**Company:** [Shield AI](https://hotfix.jobs/companies/shield-ai)
**Location:** San Diego, CA
**Role:** Product Management
**Salary:** $190k – $290k/yr
**Experience:** 7+ years
**Skills:** MLOps, Foundation Models, Robotics Models, Multimodal Models, Data Pipelines, Cloud Training, Model Governance, Edge Deployment, Synthetic Data Generation, Simulation, Gpu Infrastructure, Distributed Training, Data Lineage, Model Versioning, Distillation Pipelines
**Posted:** 2025-09-10

> Drives strategy and roadmap for AI platform enabling training, evaluation, and deployment of foundation models for edge autonomy. Requires 7+ years PM experience, strong engineering background, and deep AI/ML expertise including MLOps and model lifecycle management.

## Job Description

## 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.

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