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 Team

Rippling’s Applied ML Team is responsible for integrating AI/ML capabilities into our product suite. These integrations range from AI-powered features within existing products, like summarization tools in our applicant tracking system, to entirely AI-driven products such as our Talent Signal offering.

As our product ecosystem evolves, we are establishing a dedicated team to build and manage core platform components for AI. This team will oversee critical aspects of ML pipelines, large language models (LLMs), and search infrastructure. The role blends software engineering with infrastructure expertise, and early team members will play a pivotal role in shaping the AI platform company-wide. Our primary stakeholders are ML and software engineers within the Applied ML team, as well as engineers across other product teams.

About the Role

As an engineer working on practical applications of large language models (LLMs), search and ML models, you will own the design and implementation of the platform that trains, serves and integrates these models into our products. 

You will work closely with product teams across Rippling with the ultimate goal of building services/tools that help our customers operate their businesses more effectively. The work is necessarily cross-functional – successful individuals on our team have an unusually high degree of autonomy, strong customer empathy and an ability to drive outcomes across the entire engineering stack. Your expertise will contribute to the advancement of our organization's foundational AI capabilities and drive innovation in our products and services.

Rippling’s AI Platform efforts are in the early stages. By joining us now, you will help define  the architecture and frameworks we’ll use  and where to focus our investments in AI platform.

What you will do

• Design and develop scalable platform components for data preprocessing, feature engineering, model training, and evaluation. 
• Build core components like search platform which could be used by Rippling products
• Collaborate with cross-functional teams to translate requirements into reusable platform components
• Act as an interface between product teams and ML Engineers. Build services that offer models built by ML Engineers for product teams.
• Stay up-to-date with the latest research in ML and related fields, and apply this knowledge to improve Rippling products.

Qualifications

• You are a seasoned software engineer – having 14+ years of industry experience building software at some (or all) levels of the stack (foundational infra, backed, ux). You should be able to point to specific products that exist today that wouldn’t have been possible without your contribution.
• You are an expert at working with AI/ML – having 3+ years of industry experience building platforms that handles data preprocessing/transformation, feature engineering/storage and model training/evaluation/deployment/serving.
• You are comfortable with hands-on programming – Rippling mostly builds in Python, but prior experience in Python is not a hard requirement for this role (JVM languages/Go/Ruby experience should be transferable).
• You have a knack for communicating complex technical ideas with clarity and precision

Additional Qualifications

• Experience developing user-facing applications that use large language models (LLMs).
• Experience with full stack software engineering (distributed systems, services, UX). The more of the stack you can span comfortably, the more effectively you’ll be able to help drive project outcomes.
• Familiarity with LLM pre-training and/or fine-tuning techniques and infrastructure.