New Grad Medical Data Annotator

Wisedocs is a fast-growing, venture-backed AI platform that transforms how insurance companies analyze claims. With ~$20M USD raised, 100+ team members globally, and 90+ customers across North America and Australia, we’re doubling revenue year over year.

Founded by experts who’ve experienced the claims process firsthand, Wisedocs combines deep domain knowledge with next‑gen AI trained on 100M+ documents. Our platform turns complex medical records into clear, structured insights—backed by expert human oversight.

Join a mission-driven team building intelligent products that cut through complexity, accelerate decision-making, and make a real impact when it matters most.

As we continue to grow, we are seeking a Medical Data Annotator to join our team. This role is designed for new graduates or early-career professionals seeking hands‑on experience working with AI systems in a fast-paced and fast-growing technology company. You will act as a subject‑matter expert embedded directly into AI workflows, evaluating, correcting, and enhancing AI‑generated medical outputs and actively seeing first‑hand how human judgment shapes machine learning outcomes.

As an integral part of our innovative and cutting‑edge environment, you will gain direct exposure to how AI products are built, improved, and scaled in a real‑world startup environment, working alongside our team of brilliant machine learning and tech engineers.

This position offers a strong foundation for career growth, helping you develop technical fluency, data literacy, and an AI‑first mindset. For individuals who demonstrate strong performance and curiosity, this role can serve as a stepping stone into more technical paths within AI operations, quality, or machine learning teams.

• Review, validate, and improve AI-generated outputs
• Identify systemic errors, edge cases, and opportunities to improve model performance
• Review and document with a strong quality focus and application of clinical experience to extract key information for medical summaries and compare data with source documents to detect clinical or technical errors
• Provide structured feedback to QA and Machine Learning teams on model behavior, error patterns, and data quality issues to directly influence training and iteration cycles
• Identify recurring data issues or annotation inefficiencies and propose workflow or tooling improvements
• Meet or exceed defined productivity and quality benchmarks in a high-throughput, AI-assisted production environment
• Other duties and projects as assigned

• Background in health sciences, clinical documentation, or medical data is valued; however, demonstrated interest in technology, AI‑enabled workflows, or data‑driven systems is essential
• Comfort working with AI-generated outputs and a mindset oriented toward leveraging automation to improve speed, consistency, and scale
• This position is ideal for candidates with health sciences or clinical backgrounds seeking to evolve their careers in a more technical direction
• This role serves as an entry point for career growth into more technical roles, including Quality, AI Operations, or Machine Learning teams, for individuals who demonstrate strong performance, technical aptitude, and curiosity

• High comfort working in web-based tools, annotation platforms, and productivity software
• Strong keyboard proficiency and ability to work efficiently in high-volume digital environments
• Ability to learn new tools quickly and adapt to evolving AI-driven workflows
• Basic understanding of how machine learning systems are trained and improved (training provided)
• Experience with data labeling, QA workflows, or structured data review is a plus
• Exceptional grammar, communication and writing skills
• High level of accuracy, attention to detail and ability to exercise flexibility and comfort making judgment calls related to AI outputs
• Ability to balance speed and accuracy in a production environment
• Ability to work independently as well as collaboratively in a team-oriented environment

• Flexible hybrid environment with the option to collaborate in-person at our…