Home › Companies › Rippling › Staff Software Engineer - AI
Staff Software Engineer - AI
Rippling · San Francisco, CA, United States · On Site · Active · $180,000–$315,000 / year · Rippling ATS
Job facts
| Field | Value |
|---|---|
| Company | Rippling |
| Title | Staff Software Engineer - AI |
| Normalized title | - |
| Department / team | Growth Operations |
| Location | San Francisco, CA, United States |
| Work model | On Site |
| Employment type | Full Time |
| Salary | $180,000–$315,000 / year |
| Status | active |
| ATS provider | Rippling ATS |
| Posted / first seen | 2025-10-31 / 2026-05-29 |
| Changed / last seen | 2026-06-06 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Rippling. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Rippling ATS. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| City jobs | Active postings in San Francisco. | Open |
| Department jobs | Active postings in Growth Operations. | Open |
| Work model jobs | Active On Site postings. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | Rippling |
| Source | 02062ba4-35e8-43e7-964f-b1ad299a2912 |
| ATS provider | Rippling ATS |
Description
company
About Rippling 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.
We prioritize candidate safety. Please be aware that all official communication will only be sent from @ Rippling.com addresses.
role
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, PostgreSQL, 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 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 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 committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email [email protected]
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.
Full job record
| Job ID | 9d6193cf81af28672dc651ae2fd6b01edf1c9c59 |
| Org ID | 95955af6-63d9-4202-8015-38114b0c4352 |
| Source ID | 02062ba4-35e8-43e7-964f-b1ad299a2912 |
| Board ID | 02062ba4-35e8-43e7-964f-b1ad299a2912 |
| Provider | rippling |
| Provider Job Key | f5063c49-fe2d-45f7-be58-9e8824f653ce |
| Title | Staff Software Engineer - AI |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | San Francisco, CA, United States |
| Department | Growth Operations |
| Team | — |
| Employment Type | full_time |
| Workplace Type | on_site |
| Remote Policy | — |
| Country | United States |
| Region | CA |
| City | San Francisco |
| Salary Raw | USD 180000-315000 YEAR |
| Salary Min | 180,000 |
| Salary Max | 315,000 |
| Salary Currency | USD |
| Salary Period | year |
| Source URL | https://ats.rippling.com/rippling/jobs/f5063c49-fe2d-45f7-be58-9e8824f653ce |
| Apply URL | https://ats.rippling.com/rippling/jobs/f5063c49-fe2d-45f7-be58-9e8824f653ce |
| First Seen At | 2026-05-29 07:06:04Z |
| Last Seen At | 2026-06-06 18:47:56Z |
| Last Checked At | 2026-06-06 18:47:56Z |
| Last Changed At | 2026-06-06 18:47:56Z |
| Inactive At | — |
| Source Posted At | 2025-10-31 17:14:15Z |
| Source Updated At | — |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=rippling/board=rippling/date=2026-06-06/2026-06-06T18-47-12-364Z-8e1cce66186ea3ee494130c0b15921dc0d71aaa37e03abd15f09e33ca1ed486c.json |
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"role": "<meta><h2 style=\"margin:24px 0px 5px;padding:0px;font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-weight:600;font-size:15pt;\"><b><strong style=\"white-space:pre-wrap;\">About the Team</strong></b></h2><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:12pt 0px;line-height:1.38;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">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.</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:12pt 0px;line-height:1.38;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">We operate on a modern Growth Services infrastructure built on FastAPI, Kubernetes, Databricks, Kafka, Snowflake, PostgreSQL, and OpenAI APIs, enabling scalable experimentation and fast iteration.</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:15pt;font-weight:400;margin:12pt 0px;line-height:1.38;padding:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:15pt;white-space:pre-wrap;\">About the Role</strong></b></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:12pt 0px;line-height:1.38;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">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.</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:12pt 0px;line-height:1.38;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">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. </span></p><h2 style=\"margin:24px 0px 5px;padding:0px;font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-weight:600;font-size:15pt;\"><b><strong style=\"white-space:pre-wrap;\">What you will do</strong></b></h2><h4 style=\"margin:12pt 0px 2pt;padding:0px;font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-weight:600;font-size:13pt;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">AI/ML Architecture & Systems Design</span></h4><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:16px 0px;line-height:1.38;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Architect, build, and optimize recommendation engines, personalization systems, and classification models for GTM automation</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Design and implement multi-LLM architectures combining OpenAI, Claude, and Databricks models for intelligent decisioning and reasoning</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Build, train, and evaluate models</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Deploy and serve models using FastAPI, Kubernetes, and async microservices, with observability built in</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Develop MLOps workflows for fine-tuning, retraining, model versioning, and automated evaluation</span></li></ul><h4 style=\"margin:12pt 0px 2pt;padding:0px;font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-weight:600;font-size:13pt;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Data Engineering & Model Pipelines</span></h4><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:16px 0px;line-height:1.38;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Design medallion data architectures (Bronze/Silver/Gold) using Databricks Delta Live Tables and CDC patterns</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Build real-time and batch data pipelines leveraging Kafka and Databricks for high-volume model inputs</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Develop and maintain embedding systems and matrix factorization-based recommendation frameworks for personalization and ranking</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Implement AI data quality and monitoring frameworks to ensure reliability and trust in model outputs</span></li></ul><h4 style=\"margin:12pt 0px 2pt;padding:0px;font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-weight:600;font-size:13pt;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">AI Reliability, Observability & Optimization</span></h4><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:16px 0px;line-height:1.38;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Implement AI observability (LangSmith, Braintrust) to track performance, bias, and drift</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Build fallback and routing systems for multi-model deployments</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Optimize cost and latency through batching, caching, and adaptive model selection</span></li></ul><h4 style=\"margin:12pt 0px 2pt;padding:0px;font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-weight:600;font-size:13pt;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Technical Leadership & Collaboration</span></h4><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:16px 0px;line-height:1.38;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Lead design reviews and guide architecture for AI/ML-driven systems</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Mentor engineers on LLM integration, MLOps, and recommendation systems</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Collaborate closely with product and GTM partners to translate business goals into AI-driven automation</span><br><br></li></ul><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><b><strong style=\"white-space:pre-wrap;\">What you will need</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:16px 0px;line-height:1.38;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">7+ years of software engineering experience, including 3+ years building production ML systems.</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Expertise in recommendation engines, matrix factorization, and personalization models.</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Deep experience integrating LLMs (OpenAI, Claude, etc.) into production applications.</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Hands-on experience training, evaluating, and deploying models in Databricks notebooks and Spark pipelines.</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Experience with MLOps tooling for off-the-shelf models like XGBoost, CatBoost, or LightGBM.</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Strong background in data engineering (Kafka, Spark, Databricks, PostgreSQL).</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Proven ability to architect scalable AI systems and lead end-to-end deployment.</span></li></ul><h4 style=\"margin:12pt 0px 2pt;padding:0px;font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-weight:600;font-size:13pt;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Preferred Skills</span></h4><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:16px 0px;line-height:1.38;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);margin:12pt 0px 0pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Familiarity with LangChain, LangSmith, and vector databases.</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Deep understanding of multi-LLM coordination patterns, dynamic prompt routing, and evaluation loops.</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Experience implementing AI safety, guardrails, and interpretability frameworks.</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Experience deploying containerized AI services on Kubernetes</span></li><li style=\"color:rgb(0,0,0);margin:0pt 0px 12pt;font-size:11pt;line-height:1.38;--listitem-marker-color:#000000;\"><span style=\"color:rgb(0,0,0);font-size:11pt;white-space:pre-wrap;\">Solid understanding of feature stores, experiment tracking, and online/offline evaluation</span></li></ul><h2 style=\"margin:24px 0px 5px;padding:0px;font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-weight:600;font-size:15pt;\"><b><strong style=\"white-space:pre-wrap;\">Additional Information</strong></b></h2><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><span style=\"white-space:pre-wrap;\">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 committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email [email protected]</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><span style=\"white-space:pre-wrap;\">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.</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><span style=\"white-space:pre-wrap;\">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 </span><a href=\"https://www.rippling.com/pay-transparency\" target=\"_blank\" class=\"css-173makr-linkStyle\" style=\"color:rgb(30,74,169);cursor:pointer;\"><span style=\"white-space:pre-wrap;\">here</span></a><span style=\"white-space:pre-wrap;\">.</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><span style=\"white-space:pre-wrap;\">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.</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><span style=\"white-space:pre-wrap;\">\t</span></p>",
"company": "<meta><h2 style=\"margin:24px 0px 5px;padding:0px;font-family:"Basel Grotesk",Arial,sans-serif;line-height:1.38;font-weight:600;font-size:15pt;\"><b><strong style=\"white-space:pre-wrap;\">About Rippling</strong></b></h2><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">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.</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">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.</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Based in San Francisco, CA, </span><a href=\"https://www.rippling.com/careers#:~:text=GLOBAL%20EMPLOYEES-,%241.4B%2B,-RAISED%20TO%20DATE\" target=\"_blank\" class=\"css-173makr-linkStyle\" style=\"color:rgb(30,74,169);cursor:pointer;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Rippling has raised $1.4B+</span></a><span style=\"font-size:11pt;white-space:pre-wrap;\"> 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.</span></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><br></p><p style=\"font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:0px;line-height:1.38;padding:0px;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">We prioritize candidate safety. Please be aware that all official communication will only be sent from @</span><a href=\"http://www.Rippling.com\" target=\"_blank\" class=\"css-173makr-linkStyle\" style=\"color:rgb(30,74,169);cursor:pointer;\"><span style=\"font-size:11pt;white-space:pre-wrap;\">Rippling.com</span></a><span style=\"font-size:11pt;white-space:pre-wrap;\"> addresses.</span></p>"
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