Principal Engineer, AI Infrastructure (R4941)

{
  "content_hash": "0cd19bc50515ab889ec0c8a646198404c622ab87a47353a2e4ca2a277a0e4dcb",
  "source_hash": "b603689340868801023916e86d3f66b9c3ca3ec54634322fd7b7cb447ef614fb",
  "last_changed_at": "2026-05-29T07:00:33.142Z",
  "active_status": "active"
}

{
  "language": "en",
  "location": {
    "raw": "San Francisco, California",
    "city": "San Francisco",
    "region": "CA",
    "country": "United States",
    "is_remote": false,
    "confidence": 0.85
  },
  "salary_max": 490000,
  "salary_min": 320000,
  "inferred_at": "2026-06-06T07:56:06.498Z",
  "launch_scope": {
    "reason": "english_us_canada",
    "included": true,
    "language": "en",
    "location": {
      "raw": "San Francisco, California",
      "city": "San Francisco",
      "region": "CA",
      "country": "United States",
      "is_remote": false,
      "confidence": 0.85
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": null,
  "salary_period": "year",
  "workplace_type": "on_site",
  "salary_currency": "USD"
}

{}

{
  "lists": [
    {
      "text": "What you'll do:",
      "content": "<div>\n<div>\n\n<li><strong><span data-contrast=\"auto\">Platform Ownership:</span></strong><span data-contrast=\"auto\"> Define and operate the core AI and data platform across training, simulation, data management, evaluation, and deployment. </span></li>\n<li><strong><span data-contrast=\"auto\">Compute Strategy and Infrastructure:</span></strong><span data-contrast=\"auto\"><strong> </strong>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 </span></li>\n<li><strong><span data-contrast=\"auto\">Training and Simulation Systems:</span></strong><span data-contrast=\"auto\"><strong> </strong>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. </span></li>\n<li><strong><span data-contrast=\"auto\">Data Platform:</span></strong><span data-contrast=\"auto\"> 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. </span></li>\n<li><strong><span data-contrast=\"auto\">MLOps, Evaluation, and Model Lifecycle:</span></strong><span data-contrast=\"auto\"><strong> </strong>Establish a consistent workflow for experiment tracking, model registry, artifact provenance, and automated validation. Implement evaluation and V&amp;V gates so models meet defined standards before deployment. </span></li>\n<li><strong><span data-contrast=\"auto\">Deployment and Operational Feedback:</span></strong><span data-contrast=\"auto\"><strong> </strong>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. </span></li>\n<li><strong><span data-contrast=\"auto\">Customer AI Infrastructure:</span></strong><span data-contrast=\"auto\"><strong> </strong>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. </span></li>\n<li><strong><span data-contrast=\"auto\">Platform Standardization:</span></strong><span data-contrast=\"auto\"><strong> </strong>Define common tools, interfaces, and workflows across teams. Reduce duplication while maintaining flexibility where needed. </span></li>\n<li><strong><span data-contrast=\"auto\">Cross-Team Partnership:</span></strong><span data-contrast=\"auto\"> Work directly with Hivemind and other autonomy teams to ensure the platform supports real workloads and evolves with program needs.</span></li>\n\n</div>\n</div>"
    },
    {
      "text": "Key Outcomes:",
      "content": "<div>\n<div>\n\n<li><span data-contrast=\"auto\">Faster iteration from idea to trained model to evaluated result </span></li>\n<li><span data-contrast=\"auto\">High utilization of compute resources with clear visibility into usage and cost </span></li>\n<li><span data-contrast=\"auto\">Simulation capacity that supports large-scale training without bottlenecks </span></li>\n<li><span data-contrast=\"auto\">Consistent end-to-end lifecycle: development, evaluation, deployment, monitoring, and retraining </span></li>\n<li><span data-contrast=\"auto\">Repeatable data loop: telemetry, scenario extraction, retraining, and redeployment </span><span data-ccp-props=\"{}\">&nbsp;</span></li>\n<li><span data-contrast=\"auto\">Reliable deployment of optimized models to edge systems </span></li>\n<li><span data-contrast=\"auto\">Broad platform adoption across autonomy programs </span></li>\n<li><span data-contrast=\"auto\">Repeatable approach for deploying AI infrastructure in customer environments </span><span data-ccp-props=\"{}\">&nbsp;</span></li>\n\n</div>\n<div>\n<div>\n<div>\n<p><span data-contrast=\"auto\">Representative performance targets:</span><span data-contrast=\"auto\"> </span><span data-ccp-props=\"{}\">&nbsp;</span></p>\n</div>\n<div>\n\n<li><span data-contrast=\"auto\">Training iteration cycles measured in days, not weeks </span></li>\n<li><span data-contrast=\"auto\">Sustained high utilization of GPU resources under production workloads </span><span data-ccp-props=\"{}\">&nbsp;</span></li>\n\n</div>\n</div>\n</div>\n</div>"
    },
    {
      "text": "Required qualifications:",
      "content": "<div>\n<div>\n\n<li><span data-contrast=\"auto\">Experience building and operating ML infrastructure at scale (100+ GPU clusters, distributed systems) </span></li>\n<li><span data-contrast=\"auto\">Experience defining compute strategy, including on-premise vs cloud tradeoffs, capacity planning, and cost management </span></li>\n<li><span data-contrast=\"auto\">Strong understanding of ML workloads, including foundation models, RL/MARL, simulation-based training, and fine-tuning </span></li>\n<li><span data-contrast=\"auto\">Experience building data platforms with dataset versioning, lineage, and cataloging </span></li>\n<li><span data-contrast=\"auto\">Ability to debug and resolve system issues when needed </span><span data-ccp-props=\"{}\">&nbsp;</span></li>\n\n</div>\n</div>"
    },
    {
      "text": "Preferred qualifications:",
      "content": "<div>\n<div>\n\n<li><span data-contrast=\"auto\">Experience in defense or classified environments (e.g., air-gapped systems, SCIFs) </span></li>\n<li><span data-contrast=\"auto\">Experience with simulation-heavy ML systems (robotics, autonomy, or similar domains) </span></li>\n<li><span data-contrast=\"auto\">Experience deploying and optimizing models for edge hardware </span></li>\n<li><span data-contrast=\"auto\">Familiarity with HPC systems (schedulers, parallel storage, high-speed networking) </span><span data-ccp-props=\"{}\">&nbsp;</span></li>\n\n</div>\n</div>"
    },
    {
      "text": "Why Join Us",
      "content": "<div>\n<div>\n<p><span data-contrast=\"auto\">You will define the infrastructure that supports the development and deployment of autonomy systems across Shield AI. </span><span data-ccp-props=\"{}\">&nbsp;</span></p>\n</div>\n<div>\n<p><span data-contrast=\"auto\">This role establishes the foundation for how models are trained, evaluated, and deployed, and directly impacts how quickly new capabilities are delivered into operational environments. </span><span data-ccp-props=\"{}\">&nbsp;</span></p>\n</div>\n<div>\n<p><span data-contrast=\"auto\">You will have ownership over systems and decisions that are often distributed across multiple teams at other organizations, with the opportunity to shape how AI infrastructure is built and used both internally and in customer environments. </span><span data-ccp-props=\"{}\">&nbsp;</span></p>\n</div>\n<div>\n<p><span data-ccp-props=\"{}\">&nbsp;</span></p>\n</div>\n</div>"
    }
  ],
  "country": "US",
  "createdAt": 1778742972652,
  "updatedAt": null,
  "categories": {
    "team": "Engineering",
    "location": "San Francisco, California",
    "commitment": "Full Time Employee",
    "department": "Hivemind Platform Division",
    "allLocations": [
      "San Francisco, California"
    ]
  },
  "salaryRange": {
    "max": 490000,
    "min": 320000,
    "currency": "USD",
    "interval": "per-year-salary"
  },
  "workplaceType": "onsite"
}