# Senior Search and Recommendations Engineer, Store at Apify > Hybrid · Czechia · Senior · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Aapify%3Ad66e4e99-c0ec-44fb-99e9-289c53aebbc9 (read 2026-11-11). ## Facts (observed) - Company: [Apify](https://alderhq.app/companies/apify) - Location text: Prague - Where it can be done from (inferred): Hybrid · Czechia (evidence: "Location: Prague") - Posted: 2026-11-11 (0 days ago) - First seen by alder: 2026-11-11 · last verified: 2026-11-11 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/apify/d66e4e99-c0ec-44fb-99e9-289c53aebbc9 - Skills listed (inferred): TypeScript, JavaScript, React, Next.js, Node.js, NestJS, MongoDB, Redis, AWS, Kubernetes, Terraform, LLMs, Testing ## Pay and sponsorship - Posted pay: not posted ## Description (the employer's text) Apify is the largest marketplace of trusted tools for AI that automate work on the web. Thousands of Actors get web data, generate leads, monitor social media, track competitors, and integrate with existing apps and agents. Actors are created by a global community of builders who earn from them, while Apify manages infrastructure, billing, and distribution. Learn more at apify.com. Join us to help people put the web to work. Apify can find missing children, protect consumers from fake discounts across the EU, and feed data to AI chatbots. Apify is looking for a Senior Search and Recommendations Engineer to join the Discovery team, which owns how people and agents find the right Actor in Apify Store. The foundations are already in place: recommendations, a categorization system that powers Store filtering, LLM-based offline evals, relevance metrics, and A/B testing. With the catalog growing by more than 20% month over month, we need someone who can keep search and recommendations working for both users and builders as it scales. Discovery is one of the highest-leverage problems at Apify: when users and agents land on the right tool, activation, onboarding, and retention follow. ### What you'll be working on Own Store search relevance end to end: retrieval, ranking, and query understanding. Spot where today's setup falls short and decide what to fix first. Get more out of our Recombee setup: tune scenarios, business rules, and signals so recommendations stay relevant for each user, context, and use case. Sharpen our existing evaluation stack (LLM-based evals, relevance metrics, and A/B tests) so it catches regressions early and gives the team results it can trust. Keep discovery healthy as the catalog continues to grow, refining the signals, ranking, and categorization so they hold up as new Actors arrive. Evolve the Actor category taxonomy and the classification system behind Store filtering so every Actor stays findable by browsing and by search. Make Apify Store work for AI agents as well as people, with retrieval and query understanding that return the right Actor on the first call. Shape technical direction for search and recommendations at Apify, and build the team's expertise in both. ### Who we're looking for ### Must-haves 4+ years of software engineering experience, including 2+ years owning search or recommendation relevance in production at a marketplace, e-commerce, or content platform, ideally one with a fast-growing catalog. Judgment that comes from having done this before: you can look at an existing search, ranking, or recommendation setup and tell where it's headed for trouble, and which problems to leave alone. Hands-on experience in search relevance: ranking functions, query understanding, filtering, and sorting. Experience configuring and tuning recommendation systems, including cold-start and sparse-data problems. Experience evolving category taxonomies and classification for a large, growing catalog, whether by rules, models, or LLMs. Rigor in measuring relevance: you know which metrics and evals to trust, and you've used A/B tests to change product decisions. Strong backend skills for shipping production services and data pipelines in TypeScript/Node.js. ### Nice-to-haves Embeddings, hybrid lexical and vector retrieval, and LLMs applied to retrieval, re-ranking, query rewriting, or evaluation (LLM-as-judge). Enough React/TypeScript to ship changes to Store search and listing pages yourself. Ability to explain ranking trade-offs to product, design, and data colleagues, and to turn "help users find the right tool" into measurable goals. ### Our tech stack Frontend: React.js, styled-components, Storybook, Chromatic, Cypress, Playwright Backend: TypeScript/Node.js, Nest.js, Next.js, Docusaurus, Vitest Infra: AWS Compute (Kubernetes (EKS), EC2, Lambda), Helm, Terraform, CloudFormation, MongoDB, Redis, DynamoDB, S3, GitHub Actions Monitoring: Grafana, Prometheus, OpenTelemetry, Mezmo, Sentry, PagerDuty Tools: GitHub, Notion, Google Workspace, Slack OS, code editor, and AI assistant of your choice (Claude premium with unlimited tokens, GH Copilot, Cursor, Gemini, or JetBrains AI) --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aapify%3Ad66e4e99-c0ec-44fb-99e9-289c53aebbc9 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.