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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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Design is at the center of how Rippling turns complex business software into products people can understand, trust, and use. As a Lead Product Designer on the AI Platform team, you will help define how AI shows up across Rippling’s product suite and shape the patterns, systems, and interaction models that make enterprise AI useful, reliable, and deeply integrated into work.
This is a leadership\-level individual contributor role for a designer who can operate across ambiguous, high\-leverage problem spaces. You will work across multiple product verticals, partnering closely with product, engineering, and executive leadership, aligning others to critical outcomes while helping to translate emerging AI capabilities into coherent product experiences that scale across Rippling. The work is highly visible and strategically important: you will influence not just individual features, but the future of enterprise human\-agent interaction.
The right person for this role is both strategic and hands\-on. You should be able to shape the narrative for how enterprise AI should work, while also getting deep into the details of interaction and graphic design, technical constraints, model behavior, workflows, permissions, and edge cases. In short: you should care about the interface, but understand that the hard part is often the plumbing behind it. If you are a design leader with a renewed interest in building AI things with AI tools, we want to talk to you.
What you will do
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- Define and drive the design vision for AI Platform experiences across Rippling, shaping the product concepts, interaction patterns, and principles that teams use to build AI\-native workflows.
- Help establish “how Rippling does AI” by creating reusable frameworks for AI interactions, agentic workflows, automation, human\-in\-the\-loop review, transparency, trust, permissions, and control.
- Lead large, ambiguous, cross\-product design initiatives with many dependencies across product, engineering, data, infrastructure, design, and go\-to\-market teams.
- Partner deeply with AI Platform PMs, engineering managers, product engineers, and design leaders to turn complex technical capabilities into intuitive, trustworthy user experiences.
- Work hands\-on with individual pods while also connecting patterns across the broader AI Platform roadmap and adjacent product areas.
- Translate emerging AI behaviors, model capabilities, evaluations, agentic workflows, and platform constraints into practical product patterns that can be adopted across teams.
- Create high\-fidelity prototypes, frameworks, written narratives, and decision\-making artifacts that align executives, de\-risk strategic bets, and help teams move quickly.
- Raise the craft bar for AI product experiences across Rippling by coaching designers, reviewing work, and codifying patterns into scalable guidance.
- Stay close to customers, customer\-facing teams, competitors, and frontier AI developments to identify unmet needs and inform Rippling’s long\-term AI product strategy.
- Help shape how enterprise AI looks beyond demos: grounded in real business workflows, sensitive data, approvals, accountability, and measurable customer value.
What you will need
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- At least 10 years of product design experience, including significant experience designing and shipping complex SaaS, platform, or enterprise products.
- Recent hands\-on experience designing and shipping AI products or AI\-powered features; you should be fluent in the language, trade\-offs, and constraints of modern AI product development.
- Exceptional systems thinking, with the ability to understand and design across complex product architectures, data flows, permissions, dependencies, and edge cases.
- A track record of leading high\-impact, cross\-product initiatives as a senior IC, principal designer, design director, or equivalent design leader.
- Deep interaction design craft, including the ability to create novel patterns where established conventions do not yet exist and to prove those patterns through prototypes or production work.
- Strong product judgment and strategic thinking, with the ability to frame ambiguous problems, identify high\-leverage opportunities, and influence roadmaps across multiple teams.
- Excellent executive communication, storytelling, writing, and documentation skills; you should be able to make complex ideas crisp, persuasive, and actionable for senior leaders and cross\-functional teams.
- Technical curiosity and fluency, including comfort working closely with engineers on AI systems, platform primitives, model behavior, evals, agentic workflows, MCPs, or related technical concepts.
- A bias toward shipping, learning, and iterating quickly without sacrificing quality, coherence, or user trust.
- A collaborative leadership style that builds trust across design, product, engineering, GTM, and customer\-facing teams, while constructively challenging assumptions and raising the bar.
- A strong point of view on how AI should improve enterprise software, paired with the humility to learn from customers, data, technical constraints, and the teams closest to implementation.
*If you don't meet every requirement but are excited about the role, we encourage you to apply. Skills transfer in unexpected ways, and diverse perspectives often lead to the most innovative solutions.*
About the team
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The AI Platform team is building the foundations that make Rippling’s products AI\-native. The team works across the full Rippling platform, partnering with product teams in areas like HR, IT, Finance, Spend, Travel, Payroll, Benefits, and more to bring AI capabilities into the workflows businesses rely on every day.
This team sits at the intersection of product design, AI infrastructure, product engineering, and company strategy. The work is unusually broad: designing AI patterns that can operate across many domains, many user types, and many product surfaces while still feeling coherent, trustworthy, and distinctly Rippling.
As the Lead Product Designer for AI Platform, you will be a key design partner to senior product and engineering leaders and a force multiplier for designers working on AI experiences across the company. You will engage with PMs and EMs across AI Platform, go deep with pods that are actively shipping, and help connect their work into a clear, scalable product language for AI at Rippling.
Even if you don’t meet all of the requirements listed here, we still encourage you to apply. Skills can be used in lots of different ways, and your life and professional experience may be relevant beyond what a list of requirements will capture.
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:
174,000 \- 325,000 USD per year(US Tier 1\)
156,600 \- 292,500 USD per year(US Tier 2\)
147,900 \- 276,250 USD per year(US Tier 3\)
Salary Context
This $174K-$325K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rippling, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($249K) sits 16% above the category median. Disclosed range: $174K to $325K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Rippling AI Hiring
Rippling has 12 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, AI Agent Developer. Positions span Melville, NY, US, Columbia, MD, US, Remote, US. Compensation range: $130K - $350K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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