Senior Machine Learning Engineer - AI Cloud

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.

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About the role

At Rippling, we are not just building AI features, we are building an autonomous operating system for work. We are investing heavily in the next generation of enterprise AI: intelligent background automation, systems that monitor and improve themselves, and a custom intelligence layer built on Rippling's unique data and workflows.

The opportunity is to push the boundaries of what machine learning can do in the enterprise through purpose-built ML systems that learn from Rippling's proprietary data graph to deliver compounding intelligence across every product surface. You will be joining the team that recently launched Rippling AI, the fastest-growing product in Rippling's history. The models and ML systems you build will be the intelligence powering every AI surface  a Senior Machine Learning Engineer, you will own the end-to-end ML lifecycle for projects within the AI Cloud team: problem formulation, data preparation, model development, evaluation, and production deployment.

You will partner with Staff engineers and cross-functional teammates to make Rippling's agents reliable, accurate, and continuously improving. This is a deeply hands-on role, with opportunities to lead project-level technical decisions and contribute to the team's broader ML direction.

What you will doOwn the end-to-end machine learning lifecycle for high-impact AI projects, from problem definition through production deployment and iteration.

Design and implement ML systems—including fine-tuned LLMs, RAG, reward models, and multi-agent orchestration—tailored to Rippling's enterprise domain.

Build robust evaluation and experimentation capabilities: offline benchmarks, A/B testing, and continuous monitoring of model quality.

Develop training pipelines and data flywheels that leverage Rippling's structured data graph.

Take ML techniques from research to production: evaluate where RLHF, distillation, structured decoding, and tool-use training can improve model quality, reliability, and efficiency.

Build and improve feedback loops, active learning workflows, and automated retraining pipelines.

Partner closely with Product and Platform teams to translate requirements into reliable ML systems and deliver measurable product improvements.

Contribute to design and code reviews, share ML best practices, and mentor engineers within the team.

Stay current with ML research and apply relevant advances to production systems.

What you will need5+ years of software engineering experience, including 3+ years focused on ML, with experience shipping and operating ML systems in production.

Strong practical experience with modern ML, including LLMs, transformer architectures, fine-tuning, and RAG; familiarity with RLHF or related model-alignment techniques.

Strong fundamentals in classical ML and statistics.

Hands-on proficiency with ML…