Junior Software Engineer

Role Overview

Junior Backend Developer focused on building and maintaining backend systems for data annotation, labeling, and AI model training workflows. You’ll collaborate with senior engineers, product managers, and data operations teams to deliver APIs, dataset pipelines, and quality tooling.

Responsibilities

• Annotation Tooling & APIs: Build and maintain RESTful APIs and backend services for data labeling tools and annotation interfaces.
• Dataset & Pipeline Management: Develop backend workflows to ingest, format, process, and validate large datasets (text, image, audio, or structured data) before and after annotation.
• Database & Storage Integration: Write and optimize database queries to store, query, and serve annotation metadata using SQL or NoSQL (e.g., PostgreSQL, MongoDB).
• Quality & Validation Checks: Implement validation scripts and automated quality checks to detect labeling errors, track annotator throughput, and verify data accuracy.
• Debugging & System Support: Troubleshoot API issues and dataset export/import errors; help ensure high uptime for remote annotation teams.

Required Qualifications

• Education/Experience: Bachelor’s degree in CS/Software Engineering or related quantitative field (or equivalent experience/bootcamp with a strong project portfolio).
• Core Skills: Strong JavaScript (ES6+) and Node.js fundamentals.
• APIs/Data: Experience building/consuming RESTful APIs and working with JSON.
• Databases: Experience with PostgreSQL, MySQL, or MongoDB for dataset handling and query optimization.
• Version Control: Proficiency with Git and GitHub/GitLab.
• Data Handling: Ability to parse, clean, and transform structured/unstructured data using Node.js scripts/utilities.
• Work Authorization: Authorized to work in the United States.

Nice-to-Haves

• TypeScript familiarity.
• Basic understanding of AI/ML workflows and RLHF (or data labeling platforms like Label Studio/CVAT/custom tools).
• Experience with cloud storage (e.g., AWS S3 or Google Cloud Storage) for large dataset transfers.
• Exposure to automated testing frameworks (e.g., Jest, Mocha) or Python data processing scripts.