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 at Rippling.
As a Staff Machine Learning Engineer, you will own the end-to-end ML lifecycle: problem formulation, data strategy, model development, evaluation, and production deployment. You will lead technical direction for ML initiatives across the AI org and drive the science that makes Rippling's agents reliable, accurate, and continuously improving. This is a deeply hands-on role.
What you will do
- Own the end-to-end machine learning lifecycle for high-impact AI initiatives
- Design and implement novel ML architectures (fine-tuned LLMs, RAG, reward models, multi-agent orchestration) tailored to Rippling's enterprise domain
- Build robust evaluation and experimentation infrastructure: offline benchmarks, A/B testing, and continuous monitoring of model quality Develop training pipelines and data flywheels that leverage Rippling's structured data graph
- Lead research-to-production efforts: identify where frontier techniques (RLHF, distillation, structured decoding, tool-use training) unlock step-function improvements
- Design self-improving systems: feedback loops, active learning, and automated retraining pipelines
- Partner closely with Product and Platform teams
- Mentor engineers across the org on ML best practices
- Track the frontier of ML research and translate breakthroughs into production systems
What you will need
- 8+ years of software engineering experience with 5+ years focused on ML, shipping ML systems to production at scale
- Deep expertise in modern ML: LLMs, transformer architectures, fine-tuning, RLHF, RAG
- Strong fundamentals in classical ML and statistics
- Hands-on proficiency with ML frameworks (PyTorch, JAX) and production ML infrastructure
- Experience building evaluation systems for generative AI
- Proven ability to lead complex, cross-functional technical initiatives
- Strong product instincts
- 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
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 committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email [email protected]
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.
