Principal Engineer, AI Infrastructure (R4941)

Shield AI
San Francisco, US
On-site

Who this role is best for

Aimed at senior engineers who architect AI infrastructure for defense applications in hybrid cloud and edge environments.

Best fit for

  • Experienced in scaling AI training and deployment systems across multiple programs.
    — “scale an initial architecture into a platform that supports multiple autonomy programs”
  • Engineers who balance rapid iteration with system reliability and cost control.
    — “delivering fast iteration for engineering teams while maintaining reliability, cost control”
  • Architects comfortable with multi-modal sensor data and constrained edge systems.
    — “continuously evolving multi-modal sensor data, and deployment to constrained and reliability-critical systems”

Things to consider

  • Role spans full AI lifecycle from training to edge deployment.
    — “spans the full lifecycle of autonomy development”
  • Must design for diverse deployment environments including sovereign systems.
    — “sovereign or nationally constrained environments”

How to stand out

  • Demonstrate experience optimizing model deployment for resource-constrained hardware.
    — “deployment to constrained and reliability-critical systems”
  • Showcase architecture scaling case studies from previous roles.
    — “scale an initial architecture into a platform”
  • Highlight cross-program platform standardization successes in past projects.
    — “maintaining architectural consistency as the system scales”
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · TeamLevel · Principal

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Successfully scaled initial architecture into a platform supporting multiple autonomy programs
  • Ensured efficient movement from idea to deployed capability
  • Defined how AI infrastructure extends beyond internal use
Typical background
Experience in AI and data platform engineeringBackground in autonomy systemsStrong technical leadership

Skills & requirements

Required

AI InfrastructureData PlatformAutonomy SystemsCloud ComputingMachine LearningData ManagementSimulation-driven Development

Preferred

Distributed SystemsReliability EngineeringCost Optimization

Stack & domain

DefenseAutonomy SystemsAi Infrastructure

About the role

Original posting from Shield AI via Lever

Founded in 2015, Shield AI is a venture-backed deep-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include the V-BAT and X-BAT aircraft, Hivemind Enterprise, and the Hivemind Vision product lines. With offices and facilities across the U.S., Europe, the Middle East, and the 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, and deploying optimized models to edge systems.  

A key part of this role is defining how these capabilities extend beyond internal use. This includes establishing how Shield AI delivers AI infrastructure in customer environments across on-premise, cloud, hybrid, and sovereign or nationally constrained environments.

Source: Shield AI careers (Lever)

Similar roles