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ClickUp

Machine Learning Engineer, Ranking & Retrieval

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Remote from
USA
Salary
USD 200k–250k / yr
Department
Data Science & Analytics
Employment
Full Time
Experience
Open level
Published
Apply before
24 Nov 2026
Listing views
37
Application actions
3
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AI Summary

The role, at a glance.

ClickUp is seeking a senior Machine Learning Engineer to own ranking and retrieval systems for its large-scale search platform. The role covers the complete ML lifecycle, including model training, deployment, serving, feature pipelines, and offline quality evaluation. The engineer will build hybrid lexical and vector retrieval, embedding inference, query-understanding capabilities, and permission-aware multi-tenant search. Success requires deep information-retrieval expertise, production ML experience, and close collaboration with infrastructure, AI, and backend teams.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a highly specialized senior ML engineering role involving production ranking, retrieval quality, distributed indexing, and inference at billions-of-documents scale. The engineer is expected to independently own critical search-relevance systems while coordinating across several technical teams.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$225,000
US market range (per year)$190k–$270k
AI insightThe employer discloses an annual base compensation range of $200,000–$250,000 USD, with a midpoint of $225,000. Estimated US market compensation for a senior machine learning engineer specializing in large-scale ranking and retrieval is approximately $190,000–$270,000 per year; this is an AI estimate, not verified salary research.

Core skills

Skills and capabilities most closely associated with this opportunity.

Machine Learning EngineeringInformation RetrievalLearning to RankSemantic SearchVector SearchHybrid RetrievalEmbedding ModelsNatural Language ProcessingOpenSearchElasticsearchHNSWFeature EngineeringModel DeploymentOffline EvaluationDistributed Systems
Sample interview questions
How would you design a hybrid lexical and vector retrieval pipeline for enterprise search?

I would use lexical retrieval for exact-match, freshness-sensitive, and sparse queries, alongside ANN vector retrieval for semantic recall. I would merge and deduplicate candidates, apply permissions filtering before final ranking where feasible, and use a learned ranker with lexical, semantic, behavioral, and document-quality features. I would evaluate each stage separately for recall, latency, and relevance metrics.

What offline metrics would you use to evaluate a ranking model, and how would you validate them online?

I would use NDCG, MRR, Recall@K, and precision-oriented metrics based on graded relevance labels or carefully processed interaction data. I would segment results by query type, tenant size, document recency, and permission complexity to detect regressions. Online validation would use controlled experiments measuring successful searches, downstream engagement, reformulation rate, latency, and guardrail metrics.

How would you ensure retrieval respects permissions in a multi-tenant environment?

I would model tenant and document-level access controls as mandatory retrieval constraints rather than ranking features. Candidate generation and serving should enforce isolation through filtered indexes, access-control filters, or secure post-filtering with sufficient over-fetching, depending on scale and architecture. I would add automated tests and observability for cross-tenant leakage, permission-change propagation, and authorization failures.

Describe how you would operate embedding inference for billions of documents.

I would use asynchronous, batched embedding pipelines with durable queues, idempotent processing, versioned model metadata, and incremental updates driven by document changes. The system should support backfills, sharding, retry policies, throughput monitoring, and cost-aware hardware utilization. I would also maintain dual index versions to validate a new embedding model before progressive rollout.

How would you improve query understanding for ambiguous or short user queries?

I would combine query classification, intent prediction, entity extraction, spelling correction, and query expansion informed by user context and historical behavior. For ambiguous queries, I would generate multiple candidate interpretations while preserving lexical evidence and apply ranking features that account for recent user activity and workspace context. I would monitor performance by query length and intent category to ensure improvements do not harm navigational or exact-match searches.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

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.

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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