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Zapier
Zapier

Engineer, Applied AI

Location

Remote restrictions apply
See all remote locations

Salary Estimate

N/AIconOpenNewWindows

Seniority

N/A

Tech stacks

AI
Machine Learning
Automation
+22

Permanent role
10 hours ago
Apply now

AI at Zapier

At Zapier, we build and use automation every day to make work more efficient, creative, and human. So if you’re using AI tools while applying here - that’s great! We just ask that you use them responsibly and transparently.

Check out our guidance on How to Collaborate with AI During Zapier’s Hiring Process, including how to use AI tools like ChatGPT, Claude, Gemini, or others during our hiring process - and when not to.

Hi there!

Are you excited about building the platform that makes AI and machine learning development faster, safer, and more reliable across an entire company? We’re thrilled to invite you to join Zapier’s AI Platform team as an Applied AI Engineer!

As a key member of this team, you’ll help build and evolve the shared infrastructure that powers AI and ML development across Zapier. The AI Platform team owns the common foundations that product and engineering teams rely on when building with machine learning and generative AI - including systems like our LLM proxy server, observability tooling, and ML Ops platform capabilities.

This is a highly leveraged role. Rather than building a single end-user feature, you’ll create the core systems, tooling, and standards that many teams use as their baseline for shipping intelligent products and internal AI-powered workflows. You’ll work at the intersection of platform engineering, applied AI, and developer experience to make it easier for teams across Zapier to build with LLMs and ML systems in a scalable, secure, and production-ready way.

Your work will focus heavily on LLM Ops and ML Ops: improving how models are accessed, monitored, evaluated, deployed, governed, and operated in production. You’ll help define the paved road for teams building with AI at Zapier.

If you’re passionate about large language models, machine learning systems, developer platforms, and building tools that help other engineers move faster, 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 4+ years of experience in software engineering, including experience building and operating production AI/ML systems. You bring solid engineering fundamentals, good communication skills, and a desire to build reliable systems that others can depend on.

You have at least 1 year of experience in LLM Ops, ML Ops, or adjacent platform/infrastructure work. You’re interested in the practical challenges of operating these systems, including reliability, performance, safety, and cost.

You have experience contributing to backend systems, developer tooling, internal platforms, or infrastructure that supports other engineers. You enjoy simplifying complex workflows and improving how teams build and ship software.

You have experience of working through the full lifecycle of building, testing, deploying, and scaling ML/ LLM architectures.

You are thoughtful about engineering trade-offs and are developing a strong understanding of how to balance reliability, latency, cost, quality, and maintainability in production systems.

You enjoy working collaboratively and learning from others. You’re excited to partner with more senior engineers and cross-functional teams to build reusable platform capabilities that make AI and ML development easier across the company.

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

  • Contribute to shared AI Platform capabilities that support teams building with machine learning and generative AI across Zapier.
  • You will work mostly in TypeScript & Python. Experience isn’t strictly required, but it is a big plus. Comfort with typed languages and modern backend practices is a must.
  • Help develop and maintain core services such as our LLM proxy server, platform APIs, and reusable tooling that standardize how teams access and operate models in production.
  • Build and improve parts of our LLM Ops and ML Ops stack, including observability, monitoring, evaluation workflows, and operational tooling.
  • Help design and implement systems that improve the performance, reliability, safety, and cost efficiency of AI-powered 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.
  • Evaluate emerging tools, models, and patterns in the AI ecosystem, and help determine which ones should be incorporated into Zapier’s shared platform.

Application Deadline

The anticipated application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later, or if the position is filled.

Even though we’re an all-remote company, we still need to be thoughtful about where we have Zapiens working. Check out this resource for a list of countries where we currently cannot have Zapiens permanently working.

About Zapier

👥201-500
📍San Francisco, CA
🔗Website

Zapier Service

Zapier product / service
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How does Zapier work?

With Zapier, you can integrate apps like Salesforce, Intuit, Google, and Dropbox, to move data between them automatically, so you can focus on your most important work.

Company culture

1. Default to Action

What this means: Most decisions are changeable. It's better to deliver something real today over something maybe better later.

2. Default to Transparency

What this means: When working in a distributed, worldwide team staying on the same page is tough. Sharing context, goals, objectives, and in-progress work in public helps us all achieve a common goal.

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