Machine Learning Engineer, Ranking & Retrieval
ClickUp is hiring a Machine Learning Engineer, Ranking & Retrieval for a remote role open to applicants in United States. It pays $200k-$250k a year and Level rates it ; you can apply on ClickUp.
Level's read on this job
- Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
- Pay on this posting starts in line with the typical machine learning engineer posting on Level.
- This is listed as a remote role.
- The posting mentions Opensearch, Elasticsearch and Hnsw among its tools and skills.
- ClickUp has 48 other open roles on Level.
AI in this role
Machine learning engineer owning search relevance, ranking models, and hybrid retrieval systems for an AI workspace platform.
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 Learning, or related field
5+ years of ML engineering experience focused on ranking, retrieval, or information retrieval
Proven full ML lifecycle ownership: training, deploying, and serving models in production
Hands-on ranker model training: feature engineering, pipelines, offline evaluation
Experience building hybrid (lexical + vector) retrieval systems
Experience running embedding inference at large scale
Strong query understanding fundamentals: intent modeling, query expansion
Preferred Qualifications
Permission-aware retrieval and multi-tenancy experience
Indexing large-scale user-generated content (not small or static datasets)
Hands-on experience with OpenSearch or Elasticsearch
Sharding, index management, and real-time ingestion at scale
Background in NLP, semantic search, or agentic retrieval
Experience with TypeScript in backend systems
Join Our Mission
At ClickUp, we're committed to building the future of productivity software. As our Search Engineer, you'll play a crucial role in shaping the core functionality of our platform and driving our AI initiatives forward. Join us in our mission to make the world more productive!
#LI-REMOTE
Equal Opportunity Employer
ClickUp is an Equal Opportunity Employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
Privacy Notice
ClickUp collects and processes personal data in accordance with applicable data protection laws. You can find further details by viewing our Global Candidate Privacy Notice.
If you are a Philippine Job Applicant, please also see our Philippine Data Privacy Notice for further details.
Visa Sponsorship
Please note we are unable to sponsor or take over sponsorship of an employment visa for roles outside of engineering and product at this time. Sponsorship for engineering and product roles is not guaranteed, but is instead based on the business needs for that specific role at that time. Please reach out to the recruiter with any questions.
Fraud Alert
ClickUp Talent Acquisition will only initiate contact via an @clickup.com email or through our official careers portal on clickup.com. We will never request fees, payments, or sensitive personal information. Please disregard any offers received outside these channels and report them to support@clickup.com.
AI Processing Notice
ClickUp may use artificial intelligence and machine learning technologies to help review and screen candidates' employment applications against role-related criteria. These tools support, but do not replace, human decision‑making. If you have questions or need an accommodation in the recruitment process, please contact us at AskPeople@ClickUp.com.
How we rate this
Machine Learning Engineer, Ranking & Retrieval at ClickUp rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- This job starts at
- $200k
- Typical machine learning engineer job
- $199k
This posting starts close to the typical machine learning engineer job. See machine learning engineer jobs
Questions you could be asked
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where ranking was part of your work. What did you do?
- Tell me about a project where retrieval was part of your work. What did you do?
- Tell me about a project where semantic search was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: NLP, Machine learning, Ranking, Retrieval, and Semantic Search. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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