Software Engineer

Experience: 1 to 3 yearsLife at CultureMonkey:https://www.culturemonkey.io/life-at-culturemonkey/About the RoleCultureMonkey is an employee engagement platform serving HR and leadership teams globally. Our product is a multi-tenant Ruby on Rails application with a schema-per-tenant PostgreSQL setup, Elasticsearch-backed analytics, a ClickHouse warehouse, and a large background job layer.We are hiring an engineer who ships features independently and uses AI tooling as a genuine force multiplier. If your idea of AI-assisted development is pasting generated code into a PR, this is not the role for you. If it is decomposing a problem, driving an agent through it, verifying the output against tests and production data, and shipping in a third of the time, read on.What You Will DoOwn features end to end: scoping, schema design, implementation, testing, rollout behind feature flags, and monitoring after releaseWrite and maintain code across the stack: Rails models and services, Grape APIs, background jobs, Elasticsearch queries, and reporting pipelinesDesign queries and data access patterns that hold up at scaleUse AI tools to increase throughput across exploration, refactoring, test coverage, migrations, and documentation, and help set the standard for how the team uses themImprove the codebase you touch: eliminate duplication, tighten boundaries, and document non-obvious flowsWhat We Are Looking ForEngineers with 2+ years of experience building and maintaining production web applications in any language or framework. Ruby on Rails experience is a plus, not a requirement. Strong Python, Java, Go, Node.js, or PHP engineers who want to move to Rails are welcomeStrong SQL skills: joins, aggregates, indexing, query plans, and understanding why a query became slowComfortable with REST API design and authentication and authorization conceptsPractical experience with background jobs, queues, or asynchronous processingYou write tests as a matter of habit, not as a checkboxYou debug systematically: reproduce, isolate, verify, then fixAI Tooling (Non-Negotiable for This Role)Daily, demonstrated use of AI coding assistants in real production workYou can articulate where AI helps and where it actively hurts, and you have opinions formed from experienceYou verify AI output: run it, test it, review it, and reject it when it is wrongYou are interested in improving how the whole team works with these tools, not just your own outputBonus PointsMulti-tenant SaaS experience (schema-, row-, or database-level isolation)Experience with Elasticsearch, ClickHouse, Kafka, or analytics/warehouse systemsExperience with SSO/SAML, HRIS integrations, or third-party API integrations at scaleExperience building anything on top of LLM APIs: RAG, agents, evals, or internal developer toolingHow We EvaluateResume and portfolio screenTechnical discussion: system design and data modelling on a realistic problem from our domainPractical round: a scoped task in our stack. AI tools are allowed and encouraged. We will review the code with you and probe your decisionsDepth round on your past production work: an incident you owned, a scaling problem you solved, and a decision you would now make differentlyFounder/leadership discussionWhat You GetDirect product ownership with a short path from idea to productionAn engineering culture that works with AI tooling seriously, not as a noveltyA codebase with real engineering problems: multi-tenancy, scale, analytics, and integrations