Software Engineer - Applied Science

Company: Clipboard

Location: San Francisco, CA (Remote)

Salary: $180,000 - $400,000 a year

Type: Full-time

Remote: Yes

Posted: 2026-05-26

About this role

# About Clipboard

Our mission is to uplift as many communities as possible. We do this through our app-based marketplace that connects healthcare professionals with the workplaces that need amazing workers. This enables hundreds of thousands of people to achieve financial stability for themselves and their families while providing essential care to millions of people across the U.S.

Founded in 2016, we are a remote-first team of over 1,000 people building a top Y-Combinator company and have been profitable since 2022. We’re the leader in Long-Term Care staffing and are rapidly expanding into Home Health, Hospitals, and more, meaning we have more work to do than people to do it, and are growing our team to support millions more people and their communities.

## The team

Applied Science builds the quantitative systems that power Clipboard’s marketplace. The team owns pricing algorithms, auction mechanisms, ML infrastructure, and the core metrics — take rate, margins, and the drivers behind them — that the rest of the org depends on. They also partner with product teams to build out quantitative approaches to new problems as they come up. You can find more information on the team and areas of work from this page.

## What you'd be working on

  • Pricing algorithms and auction mechanisms
  • Shared marketplace metrics, automated variance detection, and notification systems
  • Causal modeling, experimentation frameworks, and analytical investigations

## What we're looking for

Engineers who do well on Applied Science are interested in marketplace economics and data science as much as they are in software engineering. They're equal parts builder and analyst — they dig into the data to understand what's actually driving a metric, reason through the cause and effect, and then go build and ship the solution. They partner with platform and data teams to build out our ML pipelines and rules engines, and they're comfortable working through tough architectural pr...

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