{"id":67926,"url":"https://alion.io/job/kapa-research-engineer-applied-ai","title":"Research Engineer, Applied AI","company":{"id":24603,"name":"kapa.ai","domain":"kapa.ai","url":"https://alion.io/company/kapa","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Work at a Startup","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["DE","FR","GB","AT","BE","HR","CZ","DK","FI","HU","IT","NL","NO","PL","ES","SE","CH","EE","LV","LT","PT","SK","SI","RU","UA","BG","IE","RO","GR","IS","LI","LU","MT"],"hiring_countries_total":33,"salary":{"min":100000,"max":170000,"currency":"USD","period":"year","gross":null,"usd_annual":170000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Machine Learning","optional":false},{"name":"RAG","optional":false}],"status":"live","first_seen_at":"2026-08-21T03:16:23Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-18T08:27:53Z","board_verified":true,"closed_at":null,"days_open":58,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":58},"description":"Kapa makes technical knowledge instantly accessible through AI assistants. As a research engineer you will work on improving Kapa’s ability to answer harder and harder technical questions. Check out Docker’s documentation (https://docs.docker.com) for a live example of what Kapa is (look for the “Ask AI” button).\nThe following challenges should excite you:\nEvaluating a RAG system in production without labelled data.\nCreating your own benchmark from scratch.\nBuilding an agentic retrieval system that can judge when to be fast and when to take more time.\nFine tuning embeddings or rerank models.\nTo solve these challenges you will:\nWork directly with the founding team and our software engineers.\nKeep up with the latest developments in the space and see how they can be applied.\nDesign and run experiments.\nYou may be a good fit if you have:\nA Master's/ PhD degree in Computer Science, Machine Learning, Mathematics, Statistics or a related field.\nA detailed understanding of machine learning, deep learning (including LLMs) and natural language processing.\nHands-on experience in training, fine-tuning and deploying large language models.\nHave prior experience working with vector databases, search indices, or other data stores for search and retrieval use cases.\nSignificant experience building evaluation systems for LLMs or search.\nFamiliarity with various information retrieval techniques, such as lexical search and dense vector search.\nThe ability to work effectively in a fast in a environment where things are sometimes loosely defined.\nWant to learn more about machine learning research.\n* 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.\nLocation: Remote within Europe. We’re a distributed team and welcome applicants based anywhere in Europe. 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