Document Review Specialist

Alignerr’s interview process is concise yet thorough. Candidates first face a timed online aptitude test that evaluates reading speed and logical reasoning. Successful applicants then complete a short annotation exercise to demonstrate attention to detail and guideline adherence. The final HR round focuses on cultural fit, availability, and expectations, with interviewers emphasizing clarity, reliability, and enthusiasm for AI‑driven projects.

Round 1: Online aptitude/reading comprehension test → Round 2: Annotation task assessment (sample document labeling) → Round 3: HR interview (fit, availability, expectations)

Alignerr promotes a results‑oriented, remote‑first culture where employees are trusted to deliver quality work without micromanagement. The company invests in continuous learning, offering regular webinars and knowledge‑sharing sessions, and encourages a healthy work‑life balance through flexible hours and generous leave policies.

Any graduate; no specific branch restriction. Must have a reliable high‑speed internet connection, a functional computer/laptop, and be able to work independently. No backlogs are allowed at the time of joining; candidates should have cleared all semesters.

Alignerr is an emerging AI‑driven technology firm that focuses on building intelligent document processing solutions for enterprises worldwide. Founded by a group of seasoned data scientists and software engineers, the company combines deep research expertise with practical product development, delivering platforms that can read, understand, and act upon unstructured and semi‑structured data. With a culture that encourages curiosity, rapid experimentation, and cross‑functional collaboration, Alignerr has quickly become a preferred partner for leading research labs and Fortune‑500 customers seeking to automate knowledge extraction. The role of Document Review Specialist sits at the heart of Alignerr’s data pipeline. As a specialist, you will be the human eye that teaches the machine how to interpret real‑world documents. Your day‑to‑day work involves reading a wide variety of texts—news articles, research reports, legal forms, financial statements, and data‑rich spreadsheets—then annotating, classifying, and extracting key entities according to detailed guidelines. This annotated data fuels the training of large language models, directly influencing the accuracy and reliability of Alignerr’s AI products. Key Responsibilities: 1. Read and comprehend diverse document types, ensuring full understanding before annotation. 2. Apply annotation guidelines to label sections, entities, dates, relationships, and other relevant data points. 3. Extract structured information from unstructured text and enter it into the designated annotation platform. 4. Perform quality checks on completed tasks to maintain a minimum accuracy threshold of 95%. 5. Provide feedback on guideline clarity and suggest improvements based on observed edge cases. 6. Manage task queues independently, meeting weekly hour commitments ranging from 10 to 40 hours. 7. Collaborate remotely with project leads to resolve ambiguities and receive updates on evolving annotation schemas. 8. Maintain confidentiality and data security standards for all processed documents. 9. Track productivity metrics and report any blockers promptly. 10. Participate in periodic training sessions to stay aligned with new AI model requirements. Tech Stack & Tools: The role primarily uses web‑based annotation platforms (e.g., Prodigy, Labelbox), spreadsheet software, and basic scripting for bulk uploads. Familiarity with version‑control concepts and cloud storage (AWS S3, Google Drive) is advantageous but not mandatory. Growth Path: Starting as a contract specialist, high performers can transition to senior annotation leads, quality assurance managers, or even data‑annotation product specialists. Alignerr encourages internal mobility, offering mentorship programs and access to its research community. Why Join Alignerr? You will contribute to cutting‑edge AI projects that shape how machines understand human knowledge, enjoy complete flexibility to work from anywhere, and receive competitive hourly compensation. The freelance model provides autonomy while still offering structured, meaningful work and the possibility of long‑term collaboration as new projects roll out.