Engineer, Applied AI
- Role
- AI / ML
- Experience
- Mid
- Employment
- Full-time
Open to Anywhere in NAMER. Set where you work from to check your eligibility.
No BS summary
Applied AI Engineer building shared AI/ML platform and LLM/ML Ops tooling. 4+ years software engineering experience required and at least 1 year in LLM Ops, ML Ops, or adjacent platform work. Work primarily involves LLM Ops/ML Ops and developer-facing platform engineering; you’ll commonly work with TypeScript and Python.
Optional skills
AI AT ZAPIER
So if you’re using AI tools while applying here - that’s great! We just ask that you use them responsibly and transparently.
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…
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:
ABOUT YOU
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. 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.
HOW TO APPLY
At Zapier, we believe that diverse perspectives and experiences make us better, which is why we have a non-standard application process designed to promote inclusion and equity. We're looking for the best fit for each of our roles, regardless of the type of companies in your background, so we encourage you to apply even if your skills and experiences don’t exactly match the job description. All we ask is that you answer a few in-depth questions in our application that would typically be asked at the start of an interview process. This helps speed things up by letting us get to know you and your skillset a bit better right out of the gate. Please be sure to answer each question; the resume and CV fields are optional.
Education is not a requirement for our roles; however, if you receive an offer, you will need to include your most recent educational experience as part of our background check process.
After you apply, you are going to hear back from us—even if we don’t see an immediate fit with our team. In fact, throughout the process, we strive to never go more than seven days without letting you know the status of your application. We know we’ll make mistakes from time to time, so if you ever have questions about where you stand or about the process, just ask your recruiter!
Zapier is an equal-opportunity employer and we're excited to work with talented and empathetic people of all identities. Zapier does not discriminate based on someone's identity in any aspect of hiring or employment as required by law and in line with our commitment to Diversity, Inclusion, Belonging and Equity. Zapier will consider all qualified applicants, including those with criminal histories, consistent with applicable laws.
Zapier prioritizes the security of our customers' information and is dedicated to adhering to all applicable data privacy laws.
Zapier is committed to inclusion. As part of this commitment, Zapier welcomes applications from individuals with disabilities and will work to provide reasonable accommodations. If reasonable accommodations are needed to participate in the job application or interview process, please contact jobs@zapier.com.
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.
What you'll do
- Contribute to shared AI Platform capabilities that support teams building with machine learning and generative AI across Zapier.
- Work on 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.
- 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 and evaluate emerging tools, models, and patterns for the AI platform.
What they require
- 4+ years of experience in software engineering, including experience building and operating production AI/ML systems.
- At least 1 year of experience in LLM Ops, ML Ops, or adjacent platform/infrastructure work.
- Experience contributing to backend systems, developer tooling, internal platforms, or infrastructure that supports other engineers.
- Experience working through the full lifecycle of building, testing, deploying, and scaling ML/LLM architectures.
- Comfort with typed languages and modern backend practices; solid engineering fundamentals and good communication skills.
Benefits
- Offers Equity
- Offers Bonus
- All-remote work (remote-first company)
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