Software Engineer - Data Platforms (USD-based pay)

At Micro1, we're building the future of remote work through AI-powered hiring. We empower global companies to scale effortlessly by matching them with top Latin American tech talent, bridging borders through intelligent vetting, seamless integration, and the shared power of a connected international workforce.

**Recruitment Process** Candidates must register and complete an interview. Micro1 will be responsible for conducting the selection process. Scope of Work Design and ship fullstack features across backend APIs, data pipelines, and React frontends. Own client-facing data delivery and platform infrastructure, including exports, access control, and reliability. Scope work around outcomes, direct coding agents—often in parallel—review changes at the architecture level, and iterate until shipped. Build and maintain services in Python (FastAPI) and TypeScript. Contribute to data modeling, schema design, and query performance across relational and document databases. Partner with research engineering, operations, and product to translate platform needs into production-ready systems. Improve the frameworks, checks, and evals that make agent-driven development faster without sacrificing quality. Troubleshoot production issues and build resilient, observable systems.

Preferred Qualifications Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field, or equivalent practical experience. 0–5 years of software engineering experience, or strong shipped projects as a recent graduate. Strong fundamentals in Python and TypeScript, with experience building CRUD applications using frameworks such as FastAPI/Flask and React. Working knowledge of databases such as PostgreSQL, MongoDB, or SQLite and core data modeling principles. Experience deploying services on a major cloud platform (GCP, AWS, or Azure). Coding agents such as Claude Code, Cursor, or Codex are your default development workflow, and you can clearly explain how you use them. Ability to turn outcomes into plans, direct agents, verify results, and identify architectural issues agents may miss. Understanding of agent loops, MCP, tool/data integrations, permissions, and security. Familiarity with context management, RAG, and evals for testing agent workflows. Preferred Exposure to RL concepts or environments, including tasks, actions, and rewards. Experience with coding data pipelines or task-authoring platforms. Familiarity with CI/CD, deployment pipelines, and DevOps fundamentals.