Staff Machine Learning Engineer
Staff ML Engineer owning end-to-end lifecycle for enterprise AI at Rippling: design novel architectures (LLMs, RAG, RLHF), build evaluation and self-improving systems, and ship production ML leveraging proprietary data graph. Requires 8+ years engineering with 5+ in ML.
About the job
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.
Skills
Machine Learning, LLMs, Transformers, Fine-Tuning, RLHF, RAG, PyTorch, JAX, Evaluation Systems, Generative AI, Multi-Agent Orchestration, Production Ml Infrastructure
Similar jobs
ML Engineering jobs
Shield AI
San Diego, CA
Develop production C++ perception capabilities for autonomous systems, spanning algorithms, libraries, integration, validation, and release. The role requires deep expertise in at least one perception domain, strong systems debugging, and experience delivering maintainable software in complex robotics or real-time environments.
Nuro
Mountain View, CA
Develop and productize online mapping models for autonomous navigation using real-world sensor data. The role requires deep ML expertise, robotics or computer vision experience, strong Python and deep learning framework skills, and a staff-level ability to deliver practical solutions.
Nuro
Mountain View, CA
Build and operate ML infrastructure for autonomy teams, including training and deployment pipelines, model observability, inference serving, and compiler platforms across hardware targets. Requires a degree, 3+ years of relevant experience, Python proficiency, and distributed-systems expertise.
Talkiatry
United States
Staff-level engineer responsible for building AI agents and automation, evaluating developer AI tools, and driving adoption across the engineering organization. Requires 8+ years of software engineering experience plus production experience with LLMs, agentic systems, and applied machine learning.
Airbnb
United States
Staff machine learning engineer leading scalable ranking, search, recommendation, and personalization systems. The role requires 9+ years of applied machine learning experience, strong programming and data engineering skills, and expertise productionizing models and pipelines.