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Company practice
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Mercury
backend
Build and own backend services in Mercury's roughly two-million-line Haskell codebase that powers banking, payments, and treasury products for over 300,000 startups, designing type-safe APIs and data models on PostgreSQL while collaborating closely with product and frontend teams. You'd ship features end-to-end against high-availability, money-movement systems where correctness is non-negotiable. A technical interview would probe your ability to model a domain with strong static types, reason about purity and effects in Haskell, and design schemas and transactions that keep financial state consistent under concurrency.
Mercury
DevOps / SRE
Lead the team that owns Mercury's build, test, and deployment pipeline, keeping CI fast and reliable across a massive Haskell monorepo where compile times and developer feedback loops are a first-order concern (Mercury maintains open-source tooling like ghciwatch and static-ls for exactly this). You'd manage engineers improving release safety, rollout tooling, and developer productivity. An interview would explore how you scale CI/CD for a large compiled codebase, design safe deployment and rollback strategies for financial services, and lead a team balancing velocity against reliability.
Mercury
fullstack
Work across Mercury's stack, writing Haskell on the server and React with TypeScript on the client to deliver complete product experiences in the banking dashboard, from account flows to dashboards startups use daily. You'd own features from API design through polished UI, balancing performance and product craft. An interview would likely cover building a small full-stack feature, designing a clean component and state model in React/TypeScript, and wiring it to a well-typed backend endpoint while reasoning about edge cases and error states.
Mercury
ML / AI
Build AI-powered features into Mercury's product, integrating large language models into workflows like transaction categorization, support automation, and financial insights while keeping outputs safe and reliable in a regulated banking context. You'd design prompts, evaluation pipelines, and the surrounding application plumbing in Haskell and TypeScript. A technical interview would probe how you structure LLM workflows, guard against hallucination and prompt injection in a fintech setting, and build evaluations to measure whether an AI feature is actually correct and trustworthy.
ExoForm не аффилирован с Mercury. Это независимая тренировочная страница.
Страницы построены на каталоге реальных инженерных ролей и описаний, чтобы интервью было ближе к настоящему раунду.
Да. ExoForm проводит живое голосовое интервью, задает уточнения и после завершения показывает оценку и разбор ответов.
Нет. Можно начать бесплатно, а позже перейти на платный план, если нужно больше тренировок.