Staff Software Engineer - AI

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

The ideal candidate will have a strong academic background in Computer Science, Statistics, or a related field, and a proven track record of success in building and deploying AI/ML systems. They should also have a passion for solving complex problems and a commitment to ethical AI practices.

Skills

• Python, R, Scala, Java, C++, or equivalent programming languages
• Knowledge of machine learning libraries such as TensorFlow, PyTorch, Scikit-Learn, or similar
• Experience with distributed computing frameworks like Apache Spark, Apache Flink, or Dask
• Experience with cloud platforms such as AWS, Google Cloud, or Azure
• Experience with database management systems such as PostgreSQL, MySQL, or MongoDB
• Experience with natural language processing (NLP) and text analysis tools
• Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras
• Experience with reinforcement learning and generative models
• Experience with distributed systems and microservices architecture
• Experience with data visualization tools such as Tableau, Power BI, or Matplotlib
• Experience with data warehousing and ETL processes
• Experience with data privacy and security best practices
• Experience with data governance and compliance standards

Benefits

Rippling offers a comprehensive benefits package that includes:

• Competitive salary and equity
• Health insurance (medical, dental, vision)
• Paid time off (vacation, sick leave, holidays)
• Flexible spending accounts (FSA, HSA)
• Retirement savings plans (401(k))
• Employee assistance programs (EAP)
• Professional development opportunities
• Regular team-building activities and events
• Work-from-home options for eligible employees

Pay

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.

This role will receive a competitive salary + benefits + equity. The salary for US-based employees will be aligned with one of the ranges below based on location; see which tier applies to your location here.

A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, and location. Final offer amounts may vary from the amounts listed below.

The pay range for this role is:

• 180,000 - 315,000 USD per year (US Tier 1)
• 160,000 - 280,000 USD per year (US Tier 2)
• 140,000 - 240,000 USD per year (US Tier 3)

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.