Software Engineer

Experience: 1 to 3 years

Life at CultureMonkey: https://www.culturemonkey.io/life\-at\-culturemonkey/

About the Role CultureMonkey 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 Do

• Own features end to end: scoping, schema design, implementation, testing, rollout behind feature flags, and monitoring after release
• Write and maintain code across the stack: Rails models and services, Grape APIs, background jobs, Elasticsearch queries, and reporting pipelines
• Design queries and data access patterns that hold up at scale
• Use AI tools to increase throughput across exploration, refactoring, test coverage, migrations, and documentation, and help set the standard for how the team uses them
• Improve the codebase you touch: eliminate duplication, tighten boundaries, and document non-obvious flows

What We Are Looking For

• Engineers 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 welcome
• Strong SQL skills: joins, aggregates, indexing, query plans, and understanding why a query became slow
• Comfortable with REST API design and authentication and authorization concepts
• Practical experience with background jobs, queues, or asynchronous processing
• You write tests as a matter of habit, not as a checkbox
• You debug systematically: reproduce, isolate, verify, then fix AI Tooling (Non-Negotiable for This Role)
• Daily, demonstrated use of AI coding assistants in real production work
• You can articulate where AI helps and where it actively hurts, and you have opinions formed from experience
• You verify AI output: run it, test it, review it, and reject it when it is wrong
• You are interested in improving how the whole team works with these tools, not just your own output

Bonus Points

• Multi-tenant SaaS experience (schema-, row-, or database-level isolation)
• Experience with Elasticsearch, ClickHouse, Kafka, or analytics/warehouse systems
• Experience with SSO/SAML, HRIS integrations, or third-party API integrations at scale
• Experience building anything on top of LLM APIs: RAG, agents, evals, or internal developer tooling

How We Evaluate

• Resume and portfolio screen
• Technical discussion: system design and data modelling on a realistic problem from our domain
• Practical round: a scoped task in our stack. AI tools are allowed and encouraged. We will review the code with you and probe your decisions
• Depth round on your past production work: an incident you owned, a scaling problem you solved, and a decision you would now make differently
• Founder/leadership discussion

What You Get

• Direct product ownership with a short path from idea to production
• An engineering culture that works with AI tooling seriously, not as a novelty
• A codebase with real engineering problems: multi-tenancy, scale, analytics, and integrations