Remote | Software Engineer, Data Platforms — $200,000–$280,000/year
About The Position
This full-time, fully remote opportunity is for a Software Engineer, Data Platforms to build and maintain production systems supporting human-data and AI operations. The role combines full-stack engineering with data-platform development and research-oriented work. The engineer owns features end-to-end across backend services, data pipelines, databases, and React-based frontends, using coding agents as a core development workflow and collaborating with research engineering, operations, product, and other technical stakeholders. The position includes ownership of production-system and data-infrastructure reliability, client-facing data delivery workflows, and implementation of platform requirements. Technical priorities may evolve with product and research needs. Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related discipline, or equivalent practical experience.
- Approximately 0–5 years of professional software engineering experience, or strong shipped projects as a recent graduate.
- Strong fundamentals in Python and TypeScript.
- Experience building CRUD applications using frameworks such as FastAPI, Flask, React, or comparable technologies.
- Working knowledge of relational or document databases, including PostgreSQL, MongoDB, SQLite, or similar systems.
- Experience deploying services to a major cloud platform such as GCP, AWS, or Azure.
- Practical experience using coding agents such as Claude Code, Cursor, Codex, or comparable tools as part of regular engineering work.
- Ability to turn desired outcomes into technical plans, direct coding agents, verify results, and identify architectural issues.
- Understanding of agent loops, MCP, tool and data integrations, permissions, and security.
Nice To Haves
- Exposure to reinforcement-learning concepts or environments.
- Experience with coding-data pipelines or task-authoring platforms.
- Familiarity with CI/CD, deployment pipelines, and DevOps fundamentals.
- Contributions to team projects, open-source software, or startup environments.
- Familiarity with context management, RAG, and evaluations for testing agent workflows.
Responsibilities
- Design and ship production-ready features across backend APIs, data pipelines, and React frontends.
- Build and maintain backend services using Python frameworks such as FastAPI or Flask.
- Develop TypeScript-based frontend and platform components.
- Own features from requirements and architecture through implementation, testing, and deployment.
- Build reliable systems supporting expert and operational workflows.
- Own client-facing data delivery workflows, including exports, access control, and platform reliability.
- Contribute to data modelling, schema design, and query-performance optimisation.
- Work across relational and document databases such as PostgreSQL, MongoDB, or SQLite.
- Build and maintain data pipelines supporting production platform requirements.
- Troubleshoot production issues and improve system resilience, observability, and reliability.
- Use coding agents as a primary development workflow for implementation and iteration.
- Scope engineering work around desired outcomes and direct multiple coding agents where appropriate.
- Review agent-generated changes at the architectural and systems level, identifying technical, security, or design issues automated workflows may miss.
- Improve frameworks, checks, and evaluations for agent-driven development while maintaining engineering quality.
- Partner with research engineering, operations, and product teams to translate platform requirements into production systems.
- Convert ambiguous product or research needs into scoped technical plans and implementation workflows.
- Work with agent loops, MCP, tool integrations, data integrations, permissions, and security considerations.
- Apply context management, RAG, and evaluation techniques when testing agent-based workflows.
- Communicate architecture decisions, trade-offs, implementation risks, and technical constraints clearly.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
