Our customers’ knowledge lives in blueprints, CAD files, spec sheets, and scanned PDFs that mix text, vector geometry, and raster content. You’ll build the models and algorithms that turn those documents into structured information, and the tools that let our agents use it. You’ll also improve the agent loop itself: how agents plan, choose tools, recover from failure, and behave consistently from one run to the next.
Most of your time goes into the algorithms and models behind the product rather than individual customer projects. Product engineers and FDEs work closest to customers and bring you the failure patterns they see. You analyze how our models and agents perform, decide where better algorithms, data, or training will pay off, and set the evaluation bar that tells us whether a change worked. You’ll join customer conversations when a problem needs your depth.
This is an applied role, not a research one. You’ll adapt and combine proven approaches, measure them against real customer data, and ship them. Your teammates range from full-stack engineers to applied ML researchers, and success is measured by what reaches production, not papers published.
This position is based in Austin, TX, onsite 3 days/week.
We want someone who has shipped ML-powered software to production, can adapt proven approaches and evaluate alternatives from other production ML systems, and measures whether a change works for customers. Ideally, you have deep expertise and strong technical opinions in at least one of these areas:
You do not need to be an expert in every area. Strong software engineering fundamentals and the ability to lead technical decisions matter more than familiarity with our exact stack. Beyond that, we look for:
Our core product includes a long-running agent loop written in Go. Frontier model APIs drive the loop, while specialized tools handle retrieval, extraction, and agent execution. These tools let us solve industrial problems that general-purpose agent harnesses struggle with.
We use a combination of open-source and internally fine-tuned models, particularly vision-language models (VLMs), hosted on KServe. Our customer data includes technical documents, high-resolution blueprints, raster images, vector geometry, CAD, and structured data.
If you want to own the models and algorithms at the center of our product, from understanding complex technical documents to keeping long-running agents reliable, and you measure success by what reaches production rather than what gets published, we want to talk. You don’t need to check every box.
Circuit is the AI execution layer for the physical economy.
Industrial and manufacturing enterprises run on expertise: the hard-won knowledge of how complex products are engineered, configured, sold, and supported. That expertise is scarce, slow to transfer, and walking out the door as a generation of experts retires. It lives in PDFs, spreadsheets, and the heads of a few people, and the hardest technical decisions wait on whoever happens to know.
Circuit puts expert judgment in everyone's hands. We capture how the best people make critical technical decisions and turn it into agents that do the judgment-heavy work, so anyone, regardless of skill level, can make the right call, from quote to install and support, with the accuracy these environments demand.
Circuit is built by operators. Our leadership team has built and scaled real industrial companies, and we are already live in production with category-leading manufacturers. We are not bolting AI onto an old playbook. We are building the layer that runs the last mile of how the physical economy executes.
Equal-Opportunity Employer
We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
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