Software Engineer, Data Platforms

Role Overview

Build AI-native data platforms that support the creation, delivery, and use of high-quality human intelligence data for advanced AI systems. In this full-stack role, you will own production features from backend services and data pipelines through the React experiences used by expert contributors. Coding agents are central to the development workflow, and you will collaborate with research engineering, operations, and product partners to deliver reliable systems.

Key Responsibilities

• Design and ship end-to-end features across backend APIs, data pipelines, and React frontends.
• Own client-facing data delivery and platform infrastructure, including exports, access controls, and reliability.
• Build and maintain Python, FastAPI, and TypeScript services.
• Define data models and schemas, and improve query performance across relational and document databases.
• Translate platform needs into production-ready systems with research engineering, operations, and product teams.
• Use coding agents, often in parallel, to execute outcome-focused work; review architecture, verify results, and iterate through launch.
• Improve the frameworks, checks, and evaluations that support high-quality agent-driven development.
• Troubleshoot production issues and build resilient, observable services.

Qualifications

• Bachelor''s degree in Computer Science, Computer Engineering, Software Engineering, a related field, or equivalent practical experience.
• 0 to 5 years of software engineering experience, or strong shipped projects as a recent graduate.
• Strong Python and TypeScript fundamentals, including experience building CRUD applications with frameworks such as FastAPI or Flask and React.
• Working knowledge of PostgreSQL, MongoDB, or SQLite, along with core data-modeling principles.
• Experience deploying services on GCP, AWS, or Azure.
• Comfort using coding agents such as Claude Code, Cursor, or Codex as a primary development workflow, with the ability to explain that workflow clearly.
• Ability to turn desired outcomes into plans, direct agents, validate results, and identify architectural issues agents may miss.
• Understanding of agent loops, MCP, tool and data integrations, permissions, and security.
• Familiarity with context management, RAG, and evaluations for testing agent workflows.

Preferred Qualifications

• Exposure to reinforcement learning concepts or environments, including tasks, actions, and rewards.
• Experience building data pipelines or task-authoring platforms.
• Familiarity with CI/CD, deployment pipelines, and DevOps fundamentals.
• Experience contributing to team projects, open-source software, or startups.

Work Terms

• Full-time, remote position.

Compensation

• Advertised annual compensation range: $200, 000 to $280, 000.
• The stated national base salary range is $120, 000 to $160, 000 per year.
• Eligible for equity compensation and, depending on role and company policies, performance-based bonuses.
• Benefits include up to 100% reimbursement for health-insurance premiums, paid time off, and a 401(k) plan with company match.