Hi there!

Are you excited about building the AI foundation that powers real product experiences? We’re thrilled to invite you to join the Data AI/ML team at Zapier as a Sr. Applied AI Engineer!

As a key member of our team, you’ll be at the heart of our efforts to make AI a core part of every Zapier product. You’ll help design and build the reusable building blocks—like LLM-powered APIs, vector search services, orchestration libraries, and evaluation tooling—that enable teams across the company to create AI agent experiences quickly and safely.

This is your chance to shape how AI is implemented at Zapier—from the raw data and infrastructure to the user-facing product experiences. You’ll collaborate with AI Engineers, Product teams and Machine Learning Engineers to make sure our AI capabilities are scalable, reliable, and easy to use. Your work will have massive impact across the entire organization.

If you’re passionate about large language models (LLMs), love building powerful developer tools, and want to help bring intelligent automation to millions of users, we’d love to meet you!

If you’re interested in advancing your career at a fast-growing, profitable, impact-driven company, then read on…

• Our Commitment to Applicants
• Culture and Values at Zapier
• Zapier Guide to Remote Work
• Zapier Code of Conduct
• Diversity and Inclusivity at Zapier

About You

Even though our job description may seem like we're looking for a specific candidate, the role inevitably ends up tailored to the person who applies and joins. Regardless of how well you feel you fit our description, we encourage you to apply if you meet these criteria:

• You have 7+ years of experience in software engineering, with at least 5 of those years dedicated to building distributed, scalable cloud based web applications. You possess strong communication skills, problem-solving abilities, and a drive to deliver outstanding customer experiences for both external users and internal stakeholders.
• You have at least 1 year of experience working with large language models (LLMs) to perform complex tasks in production environments. You experimented with build user facing leveraging agent architectures.
• You’re familiar with underlying technologies like transformer networks, attention mechanisms, and how they contribute to models’ abilities to generate coherent responses, generate function calls, and perform other language tasks.
• You have likely deployed evaluation frameworks for LLMs, with an understanding on performance, reliability, and bias assessment.
• You likely have experience with Retrieval-Augmented Generation (RAG) systems and understand how to optimize knowledge retrieval for improved model accuracy and speed. You likely have experience with different indexing and chunking strategies based on the system’s data and goals, as well as semantic search and vector databases, and how they differ from traditional retrieval methods and databases.
• You have experience of working through the full lifecycle of building, testing, deploying, and scaling LLM architectures.
• You can identify and document trade-offs made during the development process. You also have experience building with cloud infrastructure technologies.
• You love shipping to customers. You’ll be on a team focused on understanding customers' needs and translating those needs from specifications into functional, production-ready code. You know how to balance speed versus quality to support the features we build for our
• You embody our values. At Zapier, our values are at the heart of how we work together and how we think about our customers. In our remote setting, they help develop trust and ensure we work and collaborate to democratize automation.

Things You’ll Do

• Build foundational AI tooling—like reusable frontend and backend components for AI agent experiences, observability mechanisms and evaluation tooling.
• You will work mostly in TypeScript. Experience isn’t strictly required, but it is a big plus. Comfort with typed languages and modern backend practices is a must.
• Design and implement data-driven feedback loops to monitor and improve the performance, reliability, and safety of agent systems at scale.
• Proactively identify tooling gaps and work across teams to standardize best practices for building, deploying, and monitoring AI-driven experiences.
• Collaborate closely with engineers across product, infra, and data teams to ensure our AI components are reusable, well-documented, and easy to adopt company-wide.
• Experiment with emerging LLMs and retrieval strategies, helping the team evaluate when to fine-tune, swap models, or adjust orchestration logic.

Difference between an Applied AI Engineer and an ML Engineer at Zapier:

How do we discern between other AI-forward engineering roles like ML Engineers and Applied AI Engineers? Both roles benefit from backend engineering skills and experience with LLMs.

• ML engineers are expected to have more experience building data pipelines as well as experience utilizing other types of ML models beyond LLMs.
• AI engineers are expected to have more full stack engineering experience, which would typically include frontend experience, and may only have experience working with LLMs.