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About This Role
*At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. If you're a close but not exact match with the description, we hope you'll still consider applying. Want to learn more about life at Klaviyo? Visit* *klaviyo.com/careers* *to see how we empower creators to own their own destiny.*
Klaviyo's mission is to help businesses own their growth. AI is at the center of that mission — and we're building the engineering organization to match. As Director, Engineering – AI, you'll lead the teams turning our AI strategy into real, daily leverage for hundreds of thousands of customers: from the underlying AI platform and agent framework to the product experiences embedded throughout Klaviyo.
You'll own the technical vision and execution strategy for AI\-powered capabilities across Klaviyo's product surface, partnering deeply with Product, Design, ML, Data Science, and GTM to define what to build, ship it quickly and safely, and measure real impact in customer outcomes — not just model demos. This is a hands\-on, execution\-first role for a product\-minded engineering leader who is fluent in modern LLMs, agent patterns, and what it takes to bring AI capabilities to production at scale.
How You'll Make a Difference
- Own the AI engineering charter. Define the technical vision and roadmap for AI\-powered capabilities on Klaviyo's platform, and translate it into clear plans, OKRs, and milestones for your teams.
- Design and evolve the AI platform. Lead the architecture for LLM\-powered and agentic systems — orchestration, tool/function calling, retrieval and memory, evaluation harnesses, observability, and safety guardrails tuned for real\-world product workflows.
- Ship remarkable, AI\-native product experiences. Partner with Product, Design, and partner engineering teams to prioritize the highest\-value use cases, break them into shippable iterations, and launch experiences that feel intuitive and powerful — not just demos bolted onto existing UI.
- Make safety, reliability, and measurability non\-negotiable. Define success metrics and guardrails for AI systems (e.g., time\-to\-first\-value, uplift over baselines, override rates, hallucination/error rates, latency, SLOs), and build the tooling to monitor and continuously improve them in production.
- Integrate deeply with Klaviyo's data and platform stack. Collaborate with Data Platform, Production Infrastructure, and product engineering teams to ensure AI systems can safely and efficiently access the right data while respecting privacy, security, and governance constraints.
- Raise the bar on AI engineering craft. Build shared patterns, libraries, and best practices for prompt and chain design, evaluation, human\-in\-the\-loop review, and observability that other teams can reuse; champion rigorous experimentation, A/B testing, and fast feedback loops.
- Lead high\-performing engineering teams (ICs and managers). Hire, develop, and retain high\-performing, inclusive teams; set crisp expectations, coach for impact, and create a culture of urgency, accountability, and psychological safety.
- Partner across Klaviyo to land changes. Work closely with GTM, Support, and Customer Success to roll out new AI capabilities safely, enable the field, and close the loop between customer feedback, product decisions, and system improvements.
- Measure what matters. Own a clear scorecard for your area — adoption, activation, uplift vs. baselines, quality/satisfaction, AI system reliability, and developer velocity — and use it to drive prioritization and investment decisions.
- Transform workflows by putting AI at the center, building smarter systems and ways of working from the ground up, and continuously experimenting with AI tools — testing, learning, and sharing insights to keep Klaviyo ahead of the curve.
Who You Are
- An experienced, product\-minded engineering leader. \~10\+ years in software engineering, including 5\+ years leading multiple teams and managers building user\-facing products in high\-growth SaaS or similar environments. You're energized by building things customers love, not just infrastructure for its own sake.
- Hands\-on with modern AI and agentic systems. You've led teams building with LLMs, retrieval\-augmented generation, or agent frameworks — designing multi\-step flows, tool calling, memory, evaluation, and safety guardrails — even if you're not coding every day.
- A deep systems and product thinker. You can dive into architecture and data flows, but also reason from first principles about customer workflows, jobs\-to\-be\-done, and where AI can produce step\-change improvements vs. incremental shortcuts.
- An outcome\-oriented operator. You define clear success metrics and hold teams accountable to them; you're comfortable saying "no," narrowing scope, and iterating fast.
- A builder of high\-performing, inclusive teams. You've hired and developed diverse engineering teams, grown new leaders, and created cultures where people do the best work of their careers while feeling respected and included.
- An exceptional collaborator and communicator. You translate complex AI/ML and systems topics into clear narratives for non\-technical partners; you influence peers and executives, drive alignment across Product, Engineering, Data, and GTM, and model Klaviyo's values in how you work.
- Adaptable and resilient. You stay close to the work, dive deep when needed, and lead your teams through ambiguity and change with clarity and calm.
- A practitioner of AI\-first ways of working. You know how to lead your whole team to use AI in day\-to\-day engineering development cycles, and you've experimented with or practiced fully agentic coding workflows.
- You've already experimented with AI in work or personal projects, and you're excited to dive in and learn fast. You're hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.
Nice to Haves
- Experience building AI\-powered products for end users in complex SaaS workflows (e.g., martech, CRM, growth tooling, developer platforms).
- Prior work on agentic or copilot\-style experiences embedded in production products at scale.
- Background combining experimentation platforms, offline evaluation, and human review loops to improve AI systems over time.
- Experience operating in multi\-product, multi\-team environments where clear interfaces and ownership boundaries are critical.
- Comfort working from our Boston hub with distributed teams across time zones.
Why This Role Matters
AI will reshape how businesses plan, execute, and optimize across every workflow — and Klaviyo is investing to make those capabilities real, safe, and accessible to every customer, not just the most technical ones. As Director, Engineering – AI, you'll define and deliver the next generation of AI\-native experiences in our core product, shaping both how our customers work and how our teams build. You'll help ensure Klaviyo stays ahead in AI while staying true to our values: putting customers first, moving fast without shortcuts, and staying hungry and humble as we scale.
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Please see the independent bias audit report covering our use of Covey here
Get to Know Klaviyo
We're Klaviyo (pronounced clay\-vee\-oh). We empower creators to own their destiny by making first\-party data accessible and actionable like never before. We see limitless potential for the technology we're developing to nurture personalized experiences in ecommerce and beyond. To reach our goals, we need our own crew of remarkable creators—ambitious and collaborative teammates who stay focused on our north star: delighting our customers. If you're ready to do the best work of your career, where you'll be welcomed as your whole self from day one and supported with generous benefits, we hope you'll join us.
*AI fluency at Klaviyo includes responsible use of AI (including privacy, security, bias awareness, and human\-in\-the\-loop). We provide accommodations as needed.*
*By participating in Klaviyo's interview process, you acknowledge that you have read, understood, and will adhere to our* *Guidelines for using AI in the Klaviyo interview Process. For more information about how we process your personal data, see our* *Job Applicant Privacy Notice.*
*Klaviyo is committed to a policy of equal opportunity and non\-discrimination. We do not discriminate on the basis of race, ethnicity, citizenship, national origin, color, religion or religious creed, age, sex (including pregnancy), gender identity, sexual orientation, physical or mental disability, veteran or active military status, marital status, criminal record, genetics, retaliation, sexual harassment or any other characteristic protected by applicable law.*
*IMPORTANT NOTICE: Our company takes the security and privacy of job applicants very seriously. We will never ask for payment, bank details, or personal financial information as part of the application process. All our legitimate job postings can be found on our official career site. Please be cautious of job offers that come from non\-company email addresses (@klaviyo.com), instant messaging platforms, or unsolicited calls.*
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Salary Context
This $244K-$366K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Klaviyo, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($305K) sits 83% above the category median. Disclosed range: $244K to $366K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Klaviyo AI Hiring
Klaviyo has 8 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Agent Developer. Positions span Boston, MA, US, Denver, CO, US, San Francisco, CA, US. Compensation range: $144K - $366K.
Location Context
AI roles in Boston pay a median of $218,900 across 268 tracked positions. That's 19% above the national median.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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