Staff Machine Learning Engineer

Responsibilities

• 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.

Requirements

• 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.

Nice-to-Haves

• Publications in top ML venues (NeurIPS, ICML, ACL, EMNLP).
• Experience with enterprise data or knowledge graphs.