Jobs at Machinify

Lead and grow the AI/ML Engineering team, manage core AI/ML platform development, ensure production-grade service operational excellence, and develop product strategies.

The Data Scientist will build machine learning models to enhance claim audits, optimize performance, and improve healthcare decision-making by analyzing complex data sets.

As a DRG Reviewer, you will conduct DRG payment validation reviews, assess medical records, document findings, and ensure compliance with payment standards.

The Senior Software Engineer will design and build scalable data solutions, manage backend systems, and enhance performance leveraging Golang and Python. Responsibilities include optimizing ETL processes and collaborating with teams to meet evolving customer needs.

The DRG Reviewer audits MS-DRG and APR-DRG claims for accuracy, validates medical records, collaborates with team leaders, and maintains compliance with healthcare payment methodologies.

The Sr/Staff Frontend Engineer will develop new web applications, enhance UI experiences, architect solutions, and collaborate with various stakeholders to ensure a scalable and secure platform.

The Staff Data Scientist will advance machine learning models to optimize claim audits and improve business outcomes in healthcare.

The NLP Scientist will solve complex medical document understanding problems using NLP and CV methodologies, optimize models for performance, and make data-driven decisions.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

The Lead Rust Software Engineer will design and build scalable applications, mentor teams on Rust, and contribute to complex product development.

Lead backend architecture and implementation for scalable, reliable cloud-hosted web products. Mentor engineers, set standards, integrate agentic AI coding tools, design systems applying ML to large datasets, and balance customer-facing features with technical debt and performance.

The Client Success Leader at Machinify will enhance client satisfaction and operational processes, ensuring value delivery through support strategies while managing relationships and insights from healthcare data.

Own the Coordination of Benefits product line, overseeing its strategy, execution, and financial performance while collaborating with cross-functional teams to optimize outcomes.

Review facility claims and medical records to determine billing, coding, and clinical appropriateness per client policy and industry guidelines. Research policies, identify experimental/Do Not Bill/HAC charges, analyze medications and itemized bills, develop review content and references, support client initiatives and appeals, train staff, and meet productivity and accuracy targets.

Investigate health insurance claims to identify recoverable losses from third parties, document findings in systems, negotiate with members/adjusters/attorneys, review contracts, prepare expense spreadsheets, and process recovery payments.

As a Nurse Itemized Bill Reviewer, you'll assess facility insurance claims for accuracy, ensuring compliance with client policies and industry standards, and enhancing claim processing efficiency.

As a Claims Administrator, you will support claims processing, ensure data accuracy, handle incoming claim reviews, and facilitate claims routing, while collaborating with internal teams.

The Forward Deployed Engineer will work with customers to identify and solve problems, build solutions using an AI-powered platform, and advocate for product changes based on customer feedback and production outcomes.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

Lead and transform the QA function for an AI-accelerated software organization. Define technical quality strategy, CI/CD gates, test-data approaches for PHI, and automation across unit, integration, and end-to-end testing. Build and grow a high-performing quality engineering team, set release standards and exit criteria, integrate AI/LLM evaluation with MLEs, and provide auditable metrics for leadership and regulators.