AI Platform Engineer, Applied AI

About Us Circle is building the world’s leading all-in-one platform for online communities. We make it possible for creators, coaches, educators, and businesses to bring together their audience with engaging discussions, live streams, events, chat, courses, and payments — all in one place, all under their own brand. We’re proud to be a fully remote company of around 200 (and growing!) team members from 30+ countries around the world. We seek exceptional individuals around the world, set them up to do the best work of their lives, and in turn, create a meaningful impact in their own lives. We don't track hours, but we do manage for high expectations very closely. We collaborate across time zones, are highly async, and like to document a lot. Twice a year, we bring the whole company together in beautiful places around the world for our company offsites. So far, we’ve hosted offsites in Turkey, Portugal, Mexico, Thailand, Colombia, Italy, Ireland, and more, with still more to come! Check out our Careers page for more about working at Circle. About the role The Applied AI team at Circle is at the forefront of leveraging AI to drive innovation for our customers through transformative applications of large language models (LLMs). We build AI-powered features for our customers — AI agents, AI workflows, and AI copilot. As we scale these features to more customers, we're investing in robust infrastructure for measuring, diagnosing, and improving AI system speed and performance. We're looking for a strong AI Platform Engineer to help establish the foundation for this layer. This is a new focus area for us, with this being the first AI platform engineer hire at Circle. You’ll have significant ownership in shaping how we approach it. This role focuses on production AI systems — building infrastructure to measure and improve them— rather than ML research or model training. If you're excited about making AI systems work reliably at scale, this is for you. You'll build evaluation frameworks, observability tooling, and diagnostic infrastructure that tells us whether our AI systems are working well and helps identify where and how to improve them. When you find gaps, you'll prototype solutions, validate them with data, and work with the team to implement these improvements. What you'll be doing Build evaluation infrastructure to measure AI system speed and accuracy — both offline (during development) and online (in production). Create observability tooling and dashboards that surface quality metrics week-over-week. Diagnose quality gaps. When accuracy drops, trace whether it's retrieval, ranking, prompting, or something else causing the issue. Experiment with different models and agent configurations, using data to guide decisions. Prototype and validate improvements to our RAG pipeline — chunking strategies, retrieval methods, re-ranking approaches. Analyze how our customers are using our AI features to help us identify improvements or new areas for development. Work closely with other engineers to give them confidence that their changes improve quality. Help us stay up-to-date with the cutting-edge AI research, techniques, and tools. What you'll need to be successful 6+ years of experience in software engineering, data science, or ML — with a foundation in areas like search/retrieval, information extraction, NLP, or recommendation systems before transitioning to LLM-powered applications. Experience building and evaluating AI systems in production — including RAG pipelines, search / retrieval systems, LLM-powered applications, and both offline and LLM-based evaluation frameworks. Strong proficiency in Python for prototyping and experimentation. Openness to learning Ruby on Rails — our production system is built in Rails, and you'll need to integrate with and instrument the existing codebase. Comfortable building infrastructure and tooling (eval pipelines, dashboards, data processing). Deep understanding of RAG a ... (truncated, view full listing at source)