Staff Software Engineer - AI

Rippling · San Francisco, CA · 2 wk ago

On-siteProject ManagementFull-time

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.

Responsibilities

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 (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
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

Requirements

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.
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

Qualifications

PhD in Computer Science, Data Science, or related field
Experience with large-scale distributed systems and cloud platforms (AWS, GCP)
Experience with distributed machine learning frameworks (TensorFlow, PyTorch)
Experience with natural language processing and NLP techniques
Experience with reinforcement learning and deep learning
Experience with distributed databases and NoSQL databases
Experience with data streaming and stream processing technologies (Apache Kafka, Apache Flink)
Experience with data warehousing and analytics tools (Snowflake, PostgreSQL)
Experience with data visualization tools (Tableau, PowerBI)
Experience with data security and privacy (GDPR, CCPA)
Experience with data governance and compliance (ISO 27001, SOC 2)

Skills

Python, Java, Scala, C++, or similar programming languages
Knowledge of SQL and NoSQL databases
Experience with distributed computing frameworks (Spark, Hadoop)
Experience with cloud platforms (AWS, Azure, Google Cloud)
Experience with data visualization tools (Tableau, PowerBI)
Experience with data security and privacy (GDPR, CCPA)
Experience with data governance and compliance (ISO 27001, SOC 2)

Benefits

Rippling highly values having employees working in-office to foster a collaborative work environment and company culture. For office-based employees (employees who live within a defined radius of a Rippling office), Rippling considers working in the office, at least three days a week under current policy, to be an essential function of the employee's role.

Pay

Rippling is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email accommodations@rippling.com