×
Register Here to Apply for Jobs or Post Jobs. X

Software Engineer - AI Marketing San Francisco, CA

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Rippling
Full Time position
Listed on 2026-06-05
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 100000 - 125000 USD Yearly USD 100000.00 125000.00 YEAR
Job Description & How to Apply Below
Position: Staff Software Engineer - AI Marketing San Francisco, CA

About the Team

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.

What you will do
  • 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
AI Reliability, Observability & Optimization
  • 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
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, Cat Boost, or Light

    GBM.
  • Proven ability to architect scalable AI systems and lead end‑to‑end deployment.
Preferred Skills
  • Familiarity with Lang Chain, Lang Smith, 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
Additional Information

Rippling is an equal opportunity employer. We are committed to building a diverse and inclusive workforce and do not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics, Rippling is…

To View & Apply for jobs on this site that accept applications from your location or country, tap the button below to make a Search.
(If this job is in fact in your jurisdiction, then you may be using a Proxy or VPN to access this site, and to progress further, you should change your connectivity to another mobile device or PC).
 
 
 
Search for further Jobs Here:
(Try combinations for better Results! Or enter less keywords for broader Results)
Location
Increase/decrease your Search Radius (miles)
0
200
Filters
Education Level
Experience Level (years)
Posted in last:
Salary