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About This Role
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.
About the role
Rippling is building Rippling AI and the core AI Platform. These foundational systems will support AI capabilities across Payroll, Benefits, HR Products, Talent, Spend, IT, Data Cloud, and among product areas.
As a Senior Staff Software Engineer, you will own broad, ambiguous, high-impact technical charters across AI platform infrastructure. Key areas include harness engineering, eval frameworks, agent primitives, model-related infrastructure, selective model training work, sandboxing, scaling, latency, reliability, background agents, and enterprise intelligence systems.
The team is looking for extremely strong, hands-on engineers. AI/ML background is a strong plus, but not mandatory. The strongest signal is deep backend/systems engineering foundation, high intellectual horsepower, ability to move fast in ambiguity, and willingness to design, review, debug, and write code regularly.
What you will do
- Own broad AI Platform charters across multiple systems and teams.
- Lead architecture and execution for foundational systems supporting AI capabilities across Rippling.
- Build and guide work across AI platform infrastructure, harness engineering, eval frameworks, model-related infrastructure, sandboxing, scaling, latency, and reliability.
- Support AI capabilities across Payroll, Benefits, HR Products, Talent, Spend, IT, and Data Cloud.
- Lead without formal authority while staying deeply connected to execution.
- Write code, review critical code paths, debug complex production issues, prototype solutions, and make implementation-level decisions.
- Partner with Product, Infra, Platform, AI, and senior engineering stakeholders to convert ambiguous AI/product bets into scalable systems.
- Mentor Staff, Senior, and SE II engineers while raising engineering standards across design, coding, debugging, reliability, and operational excellence.
- Define technical patterns and platform primitives that can be reused by product teams across Rippling.
- Help shape the long-term technical foundation for Rippling AI.
What you will need
- 12+ years of software engineering experience, depending on quality of scope and impact.
- Strong backend, platform, infrastructure, distributed systems, or systems engineering foundation.
- Deep hands-on coding ability; must be able to design, review, debug, and write code regularly.
- Strong programming knowledge in one or more languages such as Python, Java, Go/Golang, C++, Scala, Kotlin, or C#.
- Proven experience owning broad, ambiguous technical charters across multiple systems or teams.
- Strong architecture and system design depth across large-scale production systems.
- Strong technical judgment across scale, reliability, latency, observability, APIs, data modeling, concurrency, and production operations.
- Ability to operate as a builder-leader: someone who can define direction, align stakeholders, and still build.
- Ability to influence and lead without formal authority while staying close to implementation.
- High intellectual horsepower, fast learning ability, and comfort operating in ambiguity.
- Strong product/platform judgment and ability to connect technical decisions to business and customer impact.
- Ability to mentor and elevate engineers across levels.
- AI/ML background is a strong plus, but not mandatory; practical exposure or strong adjacent experience in AI platform infrastructure, agents, evals, ML/data infra, model infrastructure, inference, sandboxing, workflow automation, scaling, latency, or reliability is highly valuable.
The following would be highly preferable, but not required
- Prior experience building AI platform infrastructure, eval frameworks, harness systems, LLM tooling, model-quality systems, or ML/data infrastructure.
- Experience with sandboxing, inference systems, scaling, latency optimization, reliability engineering, or large-scale distributed platforms.
- Strong Python and SQL knowledge.
- Experience with Go, Java, C++, or Scala in backend, infra, platform, or distributed systems environments.
- Experience building internal platforms used by multiple product teams.
- Prior experience in high-growth / hyper-growth startups or fast-moving product-led technology companies.
- Exposure to enterprise SaaS, fintech, HR tech, payroll, benefits, compliance, IT, Data Cloud, or workflow-heavy platforms.
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.
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 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% 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 $220,000 based on 1,601 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Rippling AI Hiring
Rippling has 12 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span San Francisco, CA, US, New York, NY, US, New York, NY. Compensation range: $210K - $315K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.
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