Senior Data Scientist, Applied AI

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

Rippling’s Go-to-Market Analytics team owns a growing suite of internal AI agents and applications used daily by Sales, RevOps, and Customer Success. We’re looking for a senior applied AI builder to help evolve this stack: improving the reliability, quality, and user experience of existing agents while designing and shipping new AI workflows that automate high-leverage GTM processes, surface better business insights, and help teams move faster.

This is a hands-on, high-ownership role on a small team that blends applied AI product development with core data science, ML, and analytics work. You will work across the full applied AI stack: backend systems, data and context pipelines, agent workflows, internal product experiences, and the evaluation and observability systems that make AI quality measurable.

What you will do

• Build, launch, and improve AI agents, workflows, and internal applications used by Rippling’s GTM teams.
• Design new agent workflows involving retrieval, tool use, structured context, multi-step reasoning, and human-in-the-loop review.
• Own full-stack feature development for internal AI products, from Python/FastAPI backend services and APIs to Next.js/TypeScript frontend experiences.
• Create SQL/Python pipelines that assemble trusted business context from GTM, product, account, and activity data.
• Apply core data science and ML techniques, including experimentation, predictive modeling, segmentation, forecasting, and product analytics, to identify opportunities, improve GTM workflows, and power AI product features.
• Build and improve the model and agent evaluation infrastructure used to measure quality, catch regressions, and guide iteration, including offline evals, golden datasets, regression tests, human review workflows, and LLM-as-judge evaluation patterns.
• Analyze production traces, usage patterns, latency, token cost, and quality signals using tools such as LangSmith or similar observability platforms.
• Debug and resolve issues across prompts, retrieval, context assembly, tool calls, integrations, latency, and system performance.
• Partner with RevOps, Sales, Customer Success, and Data Science leaders to turn analytical insights and operational pain points into shipped AI product features.
• Establish practical standards for AI quality, safety, monitoring, evaluation, and iteration across Rippling’s internal AI product suite.

What you will need

• 3–6 years of experience across data science, applied ML, software engineering, data engineering, or applied AI, including 2+ years of hands-on data science or applied ML work and 1–2 years building or operating production LLM-powered applications.
• Experience in a data science or applied ML role, including building models, designing analyses or experiments, working with business/product data, and translating findings into product or operational impact.
• Strong Python skills, with experience owning backend services, APIs, or production AI/data systems. Experience with FastAPI or an equivalent backend framework is a plus.
• Hands-on experience building production LLM systems, including prompt design, retrieval-augmented generation, tool/function calling, context management, agent orchestration, evaluation, and runtime quality controls.