Principal Engineer, AI and Data Platform Engineering (R4941)

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)