Lead the platform sub-team: set priorities, coordinate execution, and unblock the team to deliver on critical software and infrastructure initiatives.

Manage and grow the team: run 1:1s and growth conversations, give direct and timely feedback, manage performance, and mentor engineers as the team scales.

Design, build, and scale core systems spanning physics simulation, AI, digital twins, computer vision, and spatial intelligence, contributing directly to core components.

Own production pipelines: debug, monitor, and resolve issues to improve uptime and reliability.

Build and support scalable ML infrastructure: data pipelines, training systems, evaluation frameworks, and production monitoring.

Design and operate geospatial pipelines that merge heterogeneous spatial datasets into reproducible, production-grade workflows.

Strengthen CI/CD and infrastructure reliability across simulation, digital-twin, and ML pipelines.

Drive cross-functional alignment and set direction: coordinate across Applied Science and the business, communicate modeling decisions and tradeoffs, and articulate a multi-year vision for how the team’s work moves the business forward.

Core Skills (Must-Haves):

Technical leadership and ownership: managing and leading engineers and complex initiatives, delivering through others as well as hands-on, with strong planning, prioritization, and stakeholder coordination from concept through production.

5+ years building production-grade data, simulation, or modeling pipelines with end-to-end automation, monitoring, and alerting.

Strong DevOps ownership: cloud infrastructure, infrastructure-as-code, containerization, CI/CD, automated testing, observability, and incident response for systems your team runs.

Applied Science DevOps Lead

Responsibilities

Qualification

Preferred

Benefits

Company

StandInsurance

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