Software Engineer - AI

About Rippling

Rippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. For the first time ever, you can manage and automate every part of the employee lifecycle in a single system.

Take onboarding, for example. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third-party apps like Slack and Microsoft 365—all within 90 seconds.

Based in San Francisco, CA, Rippling has raised $1.4B+ from the world’s top investors—including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock—and was named one of America s best startup employers by Forbes.

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The Growth Engineering team builds world-class products, data infrastructure, and AI systems powering Rippling’s market intelligence and GTM operations. The team works cross-functionally with sales, marketing, Applied AI, and data engineering teams to design systems that amplify Rippling’s high-performance GTM engine — from recommendation models and enrichment pipelines to AI-driven workflows and proprietary data funnels.

We operate on a modern Growth Services infrastructure built on FastAPI, Kubernetes, Databricks, Kafka, Snowflake, Postgre

SQL, and OpenAI APIs, enabling scalable experimentation and fast iteration.

About the Role

We’re seeking a Staff AI/ML Engineer to architect and lead development of production-grade AI systems, including recommendation engines, multi-LLM architectures, and ML pipelines. You’ll be responsible for designing systems that combine real-time data processing, ML/LLM Ops, and intelligent orchestration across Rippling’s Growth Infrastructure.

This is a hands-on engineering leadership role — you’ll own the technical strategy for AI/ML within Growth Engineering, mentor engineers, and solve some of the most complex challenges in production AI systems with immediate business impact.

• Architect, build, and optimize recommendation engines, personalization systems, and classification models for GTM automation
• Design and implement multi-LLM architectures combining OpenAI, Claude, and Databricks models for intelligent decisioning and reasoning
• Build, train, and evaluate models
• Deploy and serve models using FastAPI, Kubernetes, and async microservices, with observability built in
• Develop MLOps workflows for fine-tuning, retraining, model versioning, and automated evaluation

• Design medallion data architectures (Bronze/Silver/Gold) using Databricks Delta Live Tables and CDC patterns
• Build real-time and batch data pipelines leveraging Kafka and Databricks for high-volume model inputs
• Develop and maintain embedding systems and matrix factorization-based recommendation frameworks for personalization and ranking
• Implement AI data quality and monitoring frameworks to ensure reliability and trust in model outputs

• Implement AI observability (Lang Smith, Braintrust) to track performance, bias, and drift
• Build fallback and routing systems for multi-model deployments
• Optimize cost and latency through batching, caching, and adaptive model selection

• Lead design reviews and guide architecture for AI/ML-driven systems
• Mentor engineers on LLM integration, MLOps, and recommendation systems
• Collaborate closely with product and GTM partners to translate business goals into AI-driven automation

What you will need

• 7+ years of software engineering experience, including 3+ years building production ML systems.
• Expertise in recommendation engines, matrix factorization, and personalization models.
• Deep experience integrating LLMs (OpenAI, Claude, etc.) into production applications.
• Hands-on experience training, evaluating, and deploying models in Databricks notebooks and Spark pipelines.
• Experience with MLOps tooling for off-the-shelf models like XGBoost, Cat…