Senior Machine Learning Engineer - AI Cloud

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.

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.

• →Own 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.

• →5+ 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 frameworks such as PyTorch or JAX and production ML infrastructure.
• →Experience building evaluation systems for generative AI.
• →Demonstrated ability to independently deliver complex ML projects, make sound technical tradeoffs, and collaborate across functions.
• →Strong product instincts and an ability to connect model improvements to customer outcomes.
• →Clear, precise communication to diverse audiences.
• →Comfort with ambiguity and high velocity.
• →Publications in top ML venues (NeurIPS, ICML, ACL, EMNLP) are a plus.
• →Experience with enterprise data or knowledge graphs is a plus.

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 committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email accommodations@rippling.com

Rippling highly values having employees working in-office to foster a collaborative work environment and company culture. For office-based employees (employees who live within a defined radius of a Rippling office), Rippling considers working in the office, at least three days a week under current policy, to be an essential function of the employee's role.

This role will receive a competitive salary + benefits + equity. The salary for US-based employees will be aligned with one of the ranges below based on location; see which tier applies to your location

here

A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, and location. Final offer amounts may vary from the amounts listed below.