Senior Machine Learning Engineer - AI Cloud

Rippling•Seattle, WA

3d•$172,800 - $288,000

About The Position

As a Senior Machine Learning Engineer on Rippling’s AI Cloud team, you will develop purpose-built machine learning systems that use Rippling’s proprietary data graph to power AI across its products. You will own the end-to-end ML lifecycle for projects, from problem formulation and data preparation through model development, evaluation, deployment, and iteration. You will work with Staff engineers and cross-functional teammates to make AI agents reliable, accurate, and continuously improving. This is a hands-on role that also includes leading project-level technical decisions and contributing to the team’s broader ML direction. The team builds systems such as fine-tuned LLMs, retrieval-augmented generation (RAG), reward models, and multi-agent orchestration. The role includes taking machine learning techniques from research into production and building evaluation, experimentation, feedback, and retraining capabilities. You will partner with Product and Platform teams to deliver measurable product improvements, contribute to technical reviews and best practices, mentor engineers, and apply relevant advances in ML research.

Requirements

At least 5 years of software engineering experience, including at least 3 years focused on ML.
Experience shipping and operating ML systems in production.
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.
Ability to independently deliver complex ML projects, make sound technical tradeoffs, and collaborate across functions.
Strong product instincts and ability to connect model improvements to customer outcomes.
Clear, precise communication with diverse audiences.
Comfort with ambiguity and high velocity.

Nice To Haves

Publications in top ML venues such as NeurIPS, ICML, ACL, or EMNLP.
Experience with enterprise data or knowledge graphs.

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 evaluation and experimentation capabilities, including offline benchmarks, A/B testing, and continuous monitoring of model quality.
Develop training pipelines and data flywheels that leverage Rippling’s structured data graph.
Evaluate and apply techniques such as RLHF, distillation, structured decoding, and tool-use training to improve model quality, reliability, and efficiency.
Build and improve feedback loops, active learning workflows, and automated retraining pipelines.
Partner with Product and Platform teams to translate requirements into reliable ML systems and measurable product improvements.
Contribute to design and code reviews, share ML best practices, and mentor engineers on the team.
Stay current with ML research and apply relevant advances to production systems.