Software Engineer - AI

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.

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

• 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 Boost, or Light
  
  GBM.
• Proven ability to architect scalable AI systems and lead…