Principal Engineer, AI Infrastructure (R4941)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Childcare support

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401(k) match

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Principal Engineer, AI Infrastructure (R4941) San Francisco, California Founded in 2015, Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software and V-BAT and X-BAT aircraft. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI's technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. Job Description: Shield AI builds autonomy systems for defense applications, including air, maritime, and space platforms operating in complex and contested environments. We are establishing a centralized AI and Data Platform organization responsible for the infrastructure that underpins autonomy development across Hivemind and other programs. This team owns the systems used to train models, run simulation, manage data, and deploy models to operational environments. We are seeking a Principal Engineer that will scale an initial architecture into a platform that supports multiple autonomy programs. Success in this role requires disciplined execution, delivering fast iteration for engineering teams while maintaining reliability, cost control, and architectural consistency as the system scales. The Principal Engineer is accountable for ensuring engineers can move efficiently from idea to trained model to deployed capability, and that infrastructure decisions reflect the realities of the domain, including simulation-driven development, continuously evolving multi-modal sensor data, and deployment to constrained and reliability-critical systems. This role spans the full lifecycle of autonomy development, training foundation models, running large-scale and multi-fidelity simulation, managing training data, evaluating models,

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