# Staff Software Engineer - AI

**Company:** [Rippling](https://hotfix.jobs/companies/rippling)
**Location:** San Francisco, CA, Seattle, WA, New York, NY
**Role:** ML Engineering
**Experience:** 7+ years
**Skills:** Recommendation Engines, Matrix Factorization, OpenAI, Claude, Databricks, Spark, Kafka, FastAPI, Kubernetes, MLOps, Xgboost, Catboost, Lightgbm, Postgres, Langsmith
**Posted:** 2025-10-31

> Staff AI/ML Engineer architects production-grade AI systems including recommendation engines, multi-LLM architectures, and ML pipelines for Rippling's growth infrastructure. Requires 7+ years software engineering with 3+ years in production ML, expertise in LLMs, data engineering, and MLOps leadership.

## Job Description

## What you will do

### AI/ML Architecture & Systems Design
- 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

### Data Engineering & Model Pipelines
- 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

### AI Reliability, Observability & Optimization
- Implement AI observability (**LangSmith**, **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

### Technical Leadership & Collaboration
- 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**, **CatBoost**, or **LightGBM**
- Strong background in data engineering (**Kafka**, **Spark**, **Databricks**, **PostgreSQL**)
- Proven ability to architect scalable AI systems and lead end-to-end deployment

## Preferred Skills
- Familiarity with **LangChain**, **LangSmith**, and vector databases
- Deep understanding of multi-LLM coordination patterns, dynamic prompt routing, and evaluation loops
- Experience implementing AI safety, guardrails, and interpretability frameworks
- Experience deploying containerized AI services on **Kubernetes**
- Solid understanding of feature stores, experiment tracking, and online/offline evaluation

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**Apply:** https://hotfix.jobs/jobs/a7210433-d018-4ee4-9e2d-d4e782f05288
**Canonical:** https://hotfix.jobs/jobs/a7210433-d018-4ee4-9e2d-d4e782f05288