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As a Software Engineer (Backend/Data), you will develop data infrastructure for medical data, build interfaces for data access, and create scalable machine learning platforms. You should have a solid background in software engineering with experience in backend, data pipelining, and strong collaboration skills.

The Software Engineer (Backend/Data) at Fathom will develop data infrastructure for ingesting and normalizing medical data, build interfaces to the data, and create systems that support scalable cloud-based machine learning. The role requires strong backend coding skills and problem-solving abilities, focusing on collaboration.

The Software Engineer (Backend/Data) will develop data infrastructure to manage and normalize medical data, build interfaces, and create scalable cloud-based machine learning solutions. Key tasks include backend development and integrations, utilizing strong computer science principles, and collaborating effectively with teammates.

The role involves managing and growing client relationships, implementing Fathom’s technology in health systems, overseeing client onboarding, and converting high-profile clients into advocates. This includes driving upsell initiatives and collecting client feedback to inform product development, alongside dynamic problem-solving in a fast-paced environment.

The Risk Adjustment Coding Advisor will work with clients to ensure coding accuracy, review medical records for compliance, conduct strategic analysis, and collaborate with cross-functional teams. Responsibilities include providing education, preparing reports, and enhancing analytics.

The Risk Adjustment Auditor will review medical records for correct diagnosis code assignment, perform audits to assess coding accuracy, prepare reports, provide coding education to the engineering and product teams, and summarize changes in coding and billing rules.

The Senior Software Engineer will develop data infrastructure to process medical data and build interfaces for efficient data handling. Responsibilities include creating scalable systems for data ingestion and cloud-based machine learning, while applying knowledge of algorithms and system design.