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CoLab is an AI platform that helps mechanical engineering teams bring products to market faster by building a knowledge repository of design feedback and decisions. The company serves enterprise customers like Ford, Komatsu, and Johnson Controls, and has been recognized on Deloitte's Fast 50 and Fast 500 lists.
You'll join the Build Operator team as a Senior Backend Developer, responsible for building the backend systems that power Operator—an intelligence layer across CoLab that helps engineering teams search company knowledge, surface design decisions, answer complex questions, and generate insights from years of engineering work.
Key responsibilities include designing and building scalable retrieval pipelines, evolving AI search infrastructure, integrating structured and unstructured engineering data, and developing services that power intelligent experiences. You'll work on knowledge retrieval systems, APIs that enable AI-powered features, search quality improvements, and expanding Operator's capabilities to support intelligent reporting, dashboards, and engineering insights. The role involves collaborating with AI, Product, Design, and Engineering teams, mentoring teammates through architecture discussions and code reviews, and improving reliability and observability across backend infrastructure.
This is a role for someone who enjoys solving deep backend problems—you'll spend far more time thinking about data architecture, search quality, retrieval systems, and scalable services than building user interfaces. The work is complex and often ambiguous, with opportunities to design entirely new capabilities rather than optimize existing systems.
You should have 7+ years building production backend software, strong experience with Python and modern backend development, and expertise with APIs, distributed systems, and cloud-native applications. You'll need a strong understanding of databases, system architecture, and scalable backend design, plus experience working with structured and unstructured data. Bonus experience includes building AI-powered applications, LLM workflows, retrieval systems, semantic search, or RAG architectures.
