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About This Role
About Rippling
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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.
About the role
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The Growth Engineering team builds world\-class products, data infrastructure, and AI systems powering Rippling’s market intelligence and GTM operations. We work closely 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, Redis, and LLM APIs, enabling scalable experimentation and fast iteration.
What you will do
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- Build polished frontend experiences in React and TypeScript for AI\-native GTM workflows, including chat interfaces, generated artifact viewers, skill libraries, embedded tools, and data\-rich account/customer views.
- Develop full\-stack product features across Next.js, FastAPI, Python, PostgreSQL, Redis, and data/LLM\-backed services.
- Design and improve multi\-turn AI chat experiences that maintain conversation state, tool\-call context, attachments, user memory, and follow\-up intent across long\-running GTM workflows.
- Build agentic workflows using LangChain, LangGraph, and related orchestration patterns, combining LLM reasoning with deterministic tools, structured data, retrieval, and human\-in\-the\-loop UX.
- Instrument AI workflows with LangSmith tracing, evaluation datasets, regression tests, and quality metrics so the team can measure answer quality, tool usage, latency, cost, and failure modes over time.
- Create and maintain evals for AI\-generated briefings, reports, analytics answers, HTML artifacts, and multi\-turn conversations, helping the team ship model and prompt changes with confidence.
- Partner with PMs, designers, sales, RevOps, Applied AI, and data engineering to turn ambiguous GTM problems into simple, high\-leverage product experiences.
- Own features end to end, from product behavior and API design to frontend polish, observability, testing, and production rollout.
What you will need
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- 3\+ years of professional software engineering experience, with strong frontend experience in React and TypeScript.
- Experience building full\-stack product features with modern backend frameworks, ideally Python and FastAPI or similar.
- Hands\-on experience building LLM\-powered products, especially multi\-turn chat, agent workflows, tool calling, retrieval, structured outputs, or generated artifacts.
- Experience with LangChain, LangGraph, LangSmith, or similar AI orchestration, tracing, and evaluation frameworks.
- Ability to design and run AI evals, debug model behavior, inspect traces, improve prompts/tools, and reason about quality across multi\-turn conversations.
- Strong product intuition and a high bar for UX, interaction design, frontend quality, and performance.
- Ability to reason across frontend state, backend APIs, data models, async jobs, permissions, observability, and production debugging.
- Experience working with data\-rich products, internal tools, analytics workflows, CRM systems, or GTM/sales/customer\-facing systems is a strong plus.
- Clear communication skills and the ability to collaborate with technical and non\-technical stakeholders.
- A bias toward ownership, practical execution, and building systems that make users meaningfully faster and better at their jobs.
Additional Information
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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.
The pay range for this role is:
135,000 \- 225,000 USD per year(US Tier 1\)
Salary Context
This $135K-$225K range is below the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Rippling, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $135K to $225K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rippling AI Hiring
Rippling has 19 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer, Data Engineer. Positions span Remote, US, New York, NY, US, San Francisco, CA, US. Compensation range: $60K - $330K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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