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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - AI Cloud - **Company:** CO-RIPPLING LLC - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $198,000.0 - $330,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Cloud Computing, Graph Database, Machine Learning, Performance Tuning, Tensorflow, Software Deployment, Software Engineering, Enterprise Data Management, Pytorch, Large Language Models, Multi-Agent Systems, Data Strategy, Machine Learning Operations - **Published:** July 8, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=7252ba2d1ebe8378 ## About the Role * 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 ## Description 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 ## Related Videos - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)