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Apify Technologies s.r.o.

Web scraping and automation platform

Senior Search and Recommendations Engineer - Store

Backend EngineeringPosted on Nov 11
Full-timePrague, Czechia

About the Job

Requirements

  • At least 4 years of software engineering experience, including at least 2 years owning search or recommendation relevance in production at a marketplace, e-commerce, or content platform, ideally with a fast-growing catalog.
  • Ability to assess existing search, ranking, or recommendation systems, identify likely problems, and prioritize which issues to address.
  • Hands-on experience with search relevance, including ranking functions, query understanding, filtering, and sorting.
  • Experience configuring and tuning recommendation systems, including addressing cold-start and sparse-data problems.
  • Experience evolving category taxonomies and classification systems for large, growing catalogs using rules, models, or large language models.
  • Experience measuring relevance using appropriate metrics and evaluations, and using A/B tests to inform product decisions.
  • Strong backend skills for shipping production services and data pipelines in TypeScript and Node.js.

Responsibilities

  • Own Store search relevance end to end, including retrieval, ranking, and query understanding; identify shortcomings and prioritize improvements.
  • Configure and tune Recombee scenarios, business rules, and signals to keep recommendations relevant across users, contexts, and use cases.
  • Improve the evaluation stack, including large-language-model-based evaluations, relevance metrics, and A/B tests, to detect regressions and provide reliable results.
  • Maintain discovery quality as the catalog grows by refining signals, ranking, and categorization.
  • Evolve the Actor category taxonomy and classification system behind Store filtering so Actors remain findable through browsing and search.
  • Improve retrieval and query understanding so AI agents can find the right Actor on the first call.
  • Shape technical direction for search and recommendations and build the team's expertise in these areas.
  • Audit Store search, recommendations, and categorization, and present the largest risks and opportunities.
  • Review evaluations and relevance metrics and close gaps that could allow a poor ranking change to go unnoticed.
  • Ship a relevance or recommendation improvement to production and validate it with an A/B test.
  • Ship and debug changes across the discovery stack, including the Store frontend, scoring and ranking services, Recombee, and supporting data pipelines.
  • Ship changes that measurably improve a core discovery metric, such as search success, first Actor run, or activation.
  • Deliver a significant retrieval, filtering, or ranking upgrade in production.
  • Improve the category taxonomy to match how users and agents search.
  • Improve agent-facing discovery so AI agents find the right Actor more reliably, measured by first-week retention.

Desired Qualifications

  • Experience with embeddings, hybrid lexical and vector retrieval, and large language models applied to retrieval, re-ranking, query rewriting, or evaluation, including LLM-as-judge.
  • Enough React and TypeScript experience to ship changes to Store search and listing pages independently.
  • Ability to explain ranking trade-offs to product, design, and data colleagues, and translate the goal of helping users find the right tool into measurable goals.

About the Company

Apify Technologies s.r.o.

Web scraping and automation platform

Apify provides a platform for web scraping and automation with a library of over 1,400 ready-made scrapers and the option to build custom scrapers in Node.js or Python. It runs scraping jobs in a cloud-based, serverless environment and uses a large pool of datacenter and residential proxies to avoid blocks, with data stored in the cloud and options for integrations. The platform also offers managed and custom scraping and RPA services to fit client needs, all in one platform. Its goal is to help businesses collect data and automate web interactions reliably at scale, while reducing the technical burden of building and maintaining scraping pipelines.

Company size
201-500
Company stage
Early VC
Total funding
$4.5M
Headquarters
Prague, Czechia
Founded
2016

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