Machine Learning Engineer, Ranking & Retrieval

**Location** ------------ United States **Employment Type** ------------------- Full time **Location Type** ----------------- Remote **Department** -------------- Engineering **Compensation** ---------------- * Estimated Compensation $200K – $250K • Offers Equity **Salary and Benefits** *At ClickUp, we believe in transparency and fairness in compensation. The range displayed reflects the minimum and maximum target salaries for the position across* ***all*** *US locations. Please note that the actual compensation for this position may vary and is dependent on factors such as geographic location, interview performance, years of experience, education level, and specific skills. We encourage candidates to discuss compensation expectations during the interview process to ensure alignment with their qualifications and our company’s compensation philosophy.* *This position is eligible for the following benefits and perks:* * *Equity* * *401k* * *Health, Dental, and Vision insurance* * *Spending accounts* * *Life \& Disability* * *Paid parental leave* * *Flexible paid time off* * *Enhanced employee assistance program* * *Employee wellness stipend* * *Professional development stipend* OverviewApplication At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context\-driven AI. We are an AI\-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. **Machine Learning Engineer, Ranking \& Retrieval** ClickUp is seeking an experienced Machine Learning Engineer to join our Search team. You'll own the ML systems that power search relevance for millions of users and ground our AI in the right context. **Your Impact at ClickUp** You'll own the full ML lifecycle for ranking and retrieval, from training through deployment and production serving. Our search indexes large\-scale, user\-generated content across a multi\-tenant platform where permissions\-aware retrieval is critical. You'll build the systems that decide what surfaces first. **Core Responsibilities** * Train, deploy, and serve ranking models in production, owning the full ML lifecycle * Build ranker features, training pipelines, and offline evaluation frameworks * Design and scale hybrid retrieval combining lexical and vector search (including HNSW with disk offloading) * Run embedding inference at billions\-of\-documents scale * Improve query understanding through intent modeling and query expansion * Build permissions\-aware retrieval that respects multi\-tenant boundaries * Create measurement frameworks to evaluate and improve search quality * Collaborate with Search Infrastructure, AI, and backend teams to integrate ranking improvements across the platform **Technical Requirements** * Bachelor's degree in Computer Science, Machine Lear