Staff Software Engineer - Payroll OrchestrationEngineeringSan Francisco, CA
RipplingRemotePosted 11 February 2026
Tech Stack
Job Description
Current Openings
Staff Software Engineer - Payroll Orchestration
Staff Software Engineer - Payroll Orchestration
About Rippling
Rippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. For the first time ever, you can manage and automate every part of the employee lifecycle in a single system.
Take onboarding, for example. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third-party apps like Slack and Microsoft 365—all within 90 seconds.
Based in San Francisco, CA, Rippling has raised $1.4B+ from the world’s top investors—including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock—and was named one of America's best startup employers by Forbes.
We prioritize candidate safety. Please be aware that all official communication will only be sent from @Rippling.com addresses.
About the role
We're looking for a talented Distributed Systems Engineer to join our core payroll team and play a key role in designing and implementing the distributed systems that drive our core payroll engine for several major countries. You'll be responsible for building and maintaining highly scalable platforms that leverage message queues, job flow systems, bitemporal data models, and efficient data pipelines to ensure accurate and timely payroll processing.
What you will do
Design, develop, and implement distributed systems with a strong platform mindset.
Write clean, maintainable, and well-documented code.
Demonstrate expertise in concurrency control mechanisms and message ordering techniques to ensure data consistency in payroll processing.
Utilize message queues and job flow systems (e.g Temporal) to orchestrate complex workflows related to payroll engine and payments processing, guaranteeing message delivery and job execution.
Develop efficient data pipelines utilizing different transactional and analytical query engines to deliver fast data aggregates that power the payroll engine.
Leverage bitemporal data modeling techniques to effectively manage payroll data flows.
Collaboration: Work closely with cross-functional engineering teams to align on strategic goals and ensure successful project delivery.
Observability: Implement comprehensive observability practices, including monitoring, logging, and tracing, to ensure system health and performance
Resiliency and SLAs: Develop and maintain critical payroll platform systems with high availability and reliability, targeting four or five 9s uptime.
What you will need
8+ years of professional experience as a software engineer
Proficiency in a modern programming language (e.g. Go, Python).
Comfortable at developing scalable and extendable core services and primitives with good abstractions that are used in many product flows.
You have seen systems operating at a significant organizational scale.
Solid understanding of message queues and job flow systems (e.g., Temporal, Kafka)
In-depth knowledge of concurrency control mechanisms and message ordering techniques.
Experience with DB transactional (e.g., Mongo, PostgreSQL) and analytical query engines (e.g, Snowflake, Clickhouse), and optimizing queries for performance.
Excellent communication and collaboration skills.
Ability to work independently and take ownership of projects, while also thriving in a team environment.
Additional Information
Rippling is an equal opportunity employer. We are committed to building a diverse and inclusive workforce and do not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics, Rippling is commi ... (truncated, view full listing at source)
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