Software Engineer (Full-Stack)

Join to apply for theSoftware Engineer (Full-stack)role atkapa.ai.Get AI-powered advice on this job and more exclusive features.This range is provided by kapa.ai. Your actual pay will be based on your skills and experience - talk with your recruiter to learn more.Base pay range$100,**/yr - $150,**/yrKapa makes technical knowledge instantly accessible through AI assistants. As a software engineer you will work across the stack on the Kapa systems that answer thousands of developer questions per day. Check out Docker’s documentation ( for a live example of what Kapa is (look for the “Ask AI” button).In This Role, You WillWork directly with the founding team and our research engineers.Scale the infrastructure that powers the Kapa RAG engine (Python).Experiment with new features in the Kapa analytics platform (React + Python).Work on the client integrations which are used to deploy Kapa for our customers (React + Python).Give Kapa access to new kinds of data (Python).Maintain our React SDK.You May Be a Good Fit If You HaveA degree in computer science, machine learning, mathematics, statistics or a related field.3+ years of software engineering experience working on complex systems in both backend and frontend.An affinity for machine learning, deep learning (including LLMs) and natural language processing.The ability to work effectively in a fast in a environment where things are sometimes loosely defined.This is neither an exhaustive nor necessary set of attributes. Even if none of these apply to you, but you believe you will contribute to kapa.ai, please reach out.Seniority levelMid-Senior levelEmployment typeFull-timeJob functionEngineering and Information TechnologyIndustriesSoftware DevelopmentReferrals increase your chances of by 2xSign in to set job alerts for “Software Engineer” roles.#J-*****-Ljbffr

A degree in computer science, machine learning, mathematics, statistics or a related field. 3+ years of software engineering experience working on complex systems in both backend and frontend. An affinity for machine learning, deep learning (including LLMs) and natural language processing. The ability to work effectively in a fast in a environment where things are sometimes loosely defined.