Build and evolve shared AI/ML infrastructure including LLM proxy server, observability tooling, and ML Ops platform capabilities. Focus on LLM Ops and ML Ops to improve how models are accessed, monitored, evaluated, deployed, and governed in production.
102k – 287k
Remote4+ YOEML Engineering
About the role
Responsibilities
Contribute to shared AI Platform capabilities that support teams building with machine learning and generative AI across Zapier
Develop and maintain core services such as LLM proxy server, platform APIs, and reusable tooling that standardize how teams access and operate models in production
Build and improve parts of the 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 to ensure 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
Requirements
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
Strong engineering fundamentals and communication skills
Thoughtful about engineering trade-offs and developing understanding of balancing reliability, latency, cost, quality, and maintainability in production systems
Comfort with typed languages and modern backend practices
Nice-to-Haves
Experience with TypeScript and Python
Interest in practical challenges of operating AI/ML systems including reliability, performance, safety, and cost
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