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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.*
At Klaviyo, the ARIA (Applications, Resources, Intelligence, and Automations) team is building the AI systems that transform how our employees work. We are looking for a Senior AI Engineer to design, build, and scale production\-grade LLM integrations and AI\-powered automation tools from the ground up. This role sits at the intersection of applied AI research and full\-stack software engineering. You will own everything from LLM integration architecture and RAG system design to CI/CD pipeline management and production observability. If you want to build AI that real people use at scale every day, this is the role for you.
You will report into the Director of AI and App Engineering and ….
How you'll make a difference:
- Design and build LLM\-powered applications: Architect and develop production\-grade systems leveraging large language models (GPT\-4, Claude, Mistral, Llama) for internal automation, knowledge retrieval, and intelligent workflow assistance.
- Build and maintain AI integration pipelines: Design robust, scalable API integrations between AI models and internal systems such as HRIS, ticketing, CRM, and knowledge bases, ensuring reliability, low latency, and high availability.
- Develop full\-stack AI features end\-to\-end: Own the complete development of AI features across the stack from backend model orchestration and API layers to frontend interfaces that make AI accessible to non\-technical employees.
- Architect and manage CI/CD pipelines for AI systems: Build and maintain automated deployment pipelines for AI models and services, including model evaluation frameworks, A/B testing infrastructure, and safe rollout strategies.
- Implement RAG and retrieval systems: Design and build retrieval\-augmented generation systems grounded in Klaviyo internal knowledge, ensuring accuracy, relevance, and up\-to\-date responses at scale.
- Evaluate and fine\-tune AI models: Lead benchmarking, evaluation, and fine\-tuning of foundation models for Klaviyo use cases, including prompt engineering and parameter\-efficient fine\-tuning techniques.
- Establish AI engineering best practices: Define coding standards, testing frameworks, and architectural patterns for AI development at Klaviyo, enabling the team to ship reliable AI features consistently.
- Build AI observability and monitoring tooling: Instrument AI systems with latency monitoring, output quality evaluation, hallucination detection, cost dashboards, and production alerting.
- Collaborate with Product and Data Science: Work closely with PMs, data scientists, and ML engineers to translate requirements into technical implementations, maintaining a tight feedback loop between experimentation and production.
- Drive security and compliance in AI systems: Ensure AI integrations follow data privacy best practices, implement guardrails for sensitive data handling, and maintain compliance with Klaviyo security and governance standards.
Who you are:
- 3\+ years of software engineering experience, with at least 2 years focused on building AI, ML, or LLM\-powered systems in production.
- Deep expertise with large language models such as OpenAI, Anthropic, Mistral, or Llama, and experience integrating them into production applications via API.
- Strong full\-stack engineering skills across Python (FastAPI, Flask, or Django) and TypeScript or JavaScript, with experience in modern frontend frameworks.
- Proven experience designing and building CI/CD pipelines for software or ML systems using tools such as GitHub Actions, Jenkins, or CircleCI.
- Experience with RAG systems, vector databases (Pinecone, Weaviate, pgvector), and embedding models.
- Proficiency with cloud infrastructure (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and infrastructure\-as\-code tools such as Terraform or Pulumi.
- Experience with prompt engineering, model evaluation frameworks, and LLM observability tools such as LangSmith or Weights and Biases.
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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.*
By clicking "Submit Application" you consent to Klaviyo processing your Personal Data in accordance with our Job Applicant Privacy Notice. If you do not wish for Klaviyo to process your Personal Data, please do not submit an application.*You can find our Job Applicant Privacy Notice* *here* *and* *here* *(FR).*
Salary Context
This $112K-$168K range is above the median 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 16% below the category median. Disclosed range: $112K to $168K.
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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