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About the Role
Rippling is building an autonomous operating system for work, investing heavily in enterprise AI. This role focuses on developing purpose-built ML systems that learn from proprietary data to deliver compounding intelligence across all products, powering the fastest-growing product in Rippling's history. As a Senior Machine Learning Engineer, you will own the end-to-end ML lifecycle, partnering with cross-functional teams to make AI agents reliable, accurate, and continuously improving. This is a hands-on role with opportunities to lead technical decisions and influence the team's ML direction.
Responsibilities
- 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.
Requirements
- 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.
Skills
- LLMs
- Transformer architectures
- Fine-tuning
- RAG
- RLHF
- Classical ML
- Statistics
- PyTorch
- JAX
- Production ML infrastructure
- Generative AI evaluation systems
Location
- US-based
Work Type
- Office-based (at least three days a week)
Experience Level
- Senior
Salary/Compensations
- Competitive salary + benefits + equity
Benefits
- Competitive salary + benefits + equity
About the Company
- Rippling is an equal opportunity employer.
- Rippling highly values having employees working in-office to foster a collaborative work environment and company culture.
Equal Opportunity
- 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