Shield AI

2 months ago

Principal Engineer, AI Infrastructure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Lead and scale the centralized AI and Data Platform, owning the core infra for training, simulation, data management, evaluation, and deployment across on-premise, cloud, and hybrid environments. Drive compute strategy, cost-per-compute, and edge deployment while enabling MPC-style ML workloads (foundation models, RL/MARL) and end-to-end model lifecycle from training to deployment.

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Define and operate the core AI and data platform across training, simulation, data management, evaluation, and deployment.
•Own where and how workloads run across on-premise, cloud, and hybrid environments; drive capacity planning and cost-per-compute decisions.
•Build infrastructure for distributed training and large-scale simulation; ensure training and simulation systems operate together without bottlenecks.
•Ingest and manage multi-modal sensor data; establish dataset versioning, data lineage, feature storage, data cataloging, and access controls.
•Establish a repeatable workflow for experiment tracking, model registry, artifact provenance, evaluation/gates, and end-to-end lifecycle from training to deployment.

Pay and Benefits

Salary: USD 320,000 - 490,000 annually

Key Requirements

•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

Experience:DefenseAutonomyRoboticsSimulation

Skills:CommunicationProblem-solvingLeadership

Languages:English

Tech Stack:GPUOn-premiseCloudHybridSovereignRLMARLFoundation modelsDistributed systemsEdge

Company Brief

Shield AI

Develops AI-powered autonomy and software for military aircraft and drones to enable autonomous ISR and combat missions, integrating perception, navigation, and mission planning for defense customers.

Industry: Defense Technology

Company Size: Large (251 to 1,000 employees)

Growth: Scaleup

Funding: Series E+

Headquarters: San Diego, United States

Founded: 2015

Website LinkedIn

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Shield AI

2 months ago

Principal Engineer, AI Infrastructure

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Location: San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Define and operate the core AI and data platform across training, simulation, data management, evaluation, and deployment.
•Own where and how workloads run across on-premise, cloud, and hybrid environments; drive capacity planning and cost-per-compute decisions.
•Build infrastructure for distributed training and large-scale simulation; ensure training and simulation systems operate together without bottlenecks.
•Ingest and manage multi-modal sensor data; establish dataset versioning, data lineage, feature storage, data cataloging, and access controls.
•Establish a repeatable workflow for experiment tracking, model registry, artifact provenance, evaluation/gates, and end-to-end lifecycle from training to deployment.

Pay and Benefits

Salary: USD 320,000 - 490,000 annually

Key Requirements

•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

Experience:DefenseAutonomyRoboticsSimulation

Skills:CommunicationProblem-solvingLeadership

Languages:English

Tech Stack:GPUOn-premiseCloudHybridSovereignRLMARLFoundation modelsDistributed systemsEdge

Company Brief

Shield AI

Industry: Defense Technology

Company Size: Large (251 to 1,000 employees)

Growth: Scaleup

Funding: Series E+

Headquarters: San Diego, United States

Founded: 2015

Website LinkedIn