Applied Science Platform Lead

Saragossa; San Francisco (CA); On-site; USD 250,000 - 290,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

Saragossa in San Francisco, CA is seeking an Applied Science Platform Lead to run the sub-team behind its physics simulations, digital twins and AI/ML models. This player-coach role reports to the Chief Science Officer.

You will own production pipelines, DevOps for the systems your team runs, including cloud infrastructure, containerization, Kubernetes, CI/CD, observability and incident response, and guide ML infrastructure across training and inference.

Qualifications

• 5+ years building production-grade data, simulation or modeling pipelines with automation, monitoring and alerting.
• Owned DevOps for team systems: cloud infra, IaC, containers, Kubernetes, CI/CD, observability and incident response.
• Strong communicator with a bias to action, translating business needs into technical deliverables.

Responsibilities

• Lead engineers and deliver complex programs through others as well as hands-on.
• Set priorities, run 1:1s, provide feedback and grow engineers.
• Own production pipeline reliability and ML infrastructure from training to inference.
• Improve geospatial data processing and QC/annotation systems behind simulations.

Skills

Platform leadership

Production data pipelines

DevOps

Kubernetes

CI/CD

Tools

Prefect

Airflow

Dagster

SLURM

Job description

Lead the platform team powering simulation, digital twins and AI at a venture-backed insurtech.

A well-funded, venture-backed insurtech is hiring an Applied Science Platform Lead to run the sub-team behind its physics simulations, digital twins and AI/ML models. This company doesn’t price risk by zip code. It simulates how real catastrophes affect individual properties, then automates underwriting and mitigation before loss occurs. As it expands into new geographies and perils, such as hurricane, the platform underneath has to scale with it.

This is a player-coach role reporting directly to the Chief Science Officer. You’ll set priorities, run 1:1s, give direct feedback and grow your engineers, while still writing code and owning key projects yourself. You’ll own production pipeline reliability, CI/CD across simulation, digital twin and ML workflows, ML training and inference infrastructure, geospatial data processing, and the annotation and QC systems behind them. You’ll work closely with Physics Simulation Engineers, ML Engineers and peril-specific SMEs, and you’ll be the person who turns “the business needs X” into “the team builds Y.” You’ll also set a multi-year vision for where the platform goes next.

You’re the right person if you’ve led engineers and delivered complex programs through others as well as hands‑on. You have 5+ years building and maintaining production‑grade data, simulation or modeling pipelines with real automation, monitoring and alerting. You’ve owned DevOps for the systems your team runs, including cloud infrastructure, infrastructure‑as‑code, containerization, Kubernetes, CI/CD, observability and incident response. You’ve debugged distributed, data‑heavy systems across APIs, preprocessing, modeling and downstream consumers, and you’re comfortable with orchestration tools like Prefect, Airflow or Dagster and cluster compute like SLURM. You communicate clearly and briefly, you balance research depth against timelines and business impact, and you have a strong bias toward action.

Strong plus: startup or zero‑to‑one experience, hands‑on ML infrastructure (GPU training and inference, dataset construction, evaluation frameworks, production model monitoring), experience supporting physics‑based or simulation‑heavy workflows like CFD or multiphysics in production, familiarity with geospatial or Earth‑observation data, and experience working directly with business or product teams.

Compensation: $250,000-$290,000 base + equity + benefits.