# Principal Engineer, AI and Data Platform Engineering (R4941)

**Company:** [Shield AI](https://hotfix.jobs/companies/shield-ai)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $320k – $490k/yr
**Skills:** ML Infrastructure, Distributed Training, Gpu Clusters, Kubernetes, MLOps, Data Versioning, Data Lineage, Rl/Marl, Foundation Models, Model Optimization, Hpc Systems, Simulation Systems, Experiment Tracking, Model Registry, Edge Deployment
**Posted:** 2026-05-14

> Leads the design, build, and operation of AI and data platform for autonomy systems, managing training, simulation, data pipelines, MLOps, and deployment across on-premise, cloud, and edge environments. Requires deep expertise in scalable ML infrastructure and compute strategy.

## Job Description

## Responsibilities

- **Platform Ownership**: Define and operate the core AI and data platform across training, simulation, data management, evaluation, and deployment.
- **Compute Strategy and Infrastructure**: Own where and how workloads run across on-premise, cloud, and hybrid environments. Drive capacity planning, utilization, and cost-per-compute decisions, including support for classified and air-gapped systems.
- **Training and Simulation Systems**: Build infrastructure for distributed training (supervised learning, RL/MARL, foundation models) and large-scale, multi-fidelity simulation. Ensure training and simulation systems operate together without bottlenecks.
- **Data Platform**: Ingest and manage multi-modal sensor data (EO, IR, radar, EW, IMU). Establish dataset versioning, data lineage, feature storage, data cataloging, and classification-aware storage and access controls.
- **MLOps, Evaluation, and Model Lifecycle**: Establish a consistent workflow for experiment tracking, model registry, artifact provenance, and automated validation. Implement evaluation and V&V gates so models meet defined standards before deployment.
- **Deployment and Operational Feedback**: Own the pipeline from training to deployment, including model optimization (e.g., distillation, quantization, pruning), deployment to edge systems, monitoring, drift detection, and retraining triggers.
- **Customer AI Infrastructure**: Define how AI infrastructure is deployed in customer environments across on-premise, cloud, hybrid, and sovereign settings. Establish a consistent approach that avoids one-off solutions while adapting to operational constraints.
- **Platform Standardization**: Define common tools, interfaces, and workflows across teams. Reduce duplication while maintaining flexibility where needed.
- **Cross-Team Partnership**: Work directly with Hivemind and other autonomy teams to ensure the platform supports real workloads and evolves with program needs.

## Required Qualifications

- Experience building and operating ML infrastructure at scale (100+ GPU clusters, distributed systems)
- Experience defining compute strategy, including on-premise vs cloud tradeoffs, capacity planning, and cost management
- Strong understanding of ML workloads, including foundation models, RL/MARL, simulation-based training, and fine-tuning
- Experience building data platforms with dataset versioning, lineage, and cataloging
- Ability to debug and resolve system issues when needed

## Preferred Qualifications

- Experience in defense or classified environments (e.g., air-gapped systems, SCIFs)
- Experience with simulation-heavy ML systems (robotics, autonomy, or similar domains)
- Experience deploying and optimizing models for edge hardware
- Familiarity with HPC systems (schedulers, parallel storage, high-speed networking)

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