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Wellrithms

AI-driven medical billing integrity for payors

AI/ML Engineer

Applied Machine LearningUpdated on Nov 11
Full-time$160k - $180k /yr

About the Job

Requirements

  • A bachelor's degree in Computer Science, Data Science, Engineering, or a related quantitative field is required; a master's degree is preferred.
  • At least 5 years of hands-on experience building, deploying, and maintaining production machine learning, Generative AI, and agentic solutions enterprise-wide for business applications.
  • Practical knowledge of combining optical character recognition engines with natural language processing, large language models, vision language models, and foundational AI models to parse, structure, and query complex healthcare documents.
  • Expertise in the AWS ecosystem, including AWS Bedrock, SageMaker, Lambda, ECS, and EKS, and proficiency in Python, PyTorch or TensorFlow, and modern Generative AI and agentic frameworks such as Strands Agents, AgentCore, and Kiro.
  • Knowledge of algorithm design, data structures, containerization with Docker and Kubernetes, and deploying machine learning models as scalable APIs using Flask, FastAPI, or similar frameworks.
  • Hands-on experience with infrastructure as code tools such as AWS CDK or Terraform, and experience building and maintaining CI/CD pipelines such as GitHub Actions or AWS CodePipeline.
  • Experience with healthcare data structures such as insurance claims, electronic health records, medical billing codes including CPT and ICD, or EDI 837/835 feeds.
  • Understanding of HIPAA compliance, data masking, secure prompt and data handling, and AI safety guardrails.
  • Authorization to work in the United States without current or future company sponsorship.
  • Ability to work independently.

Responsibilities

  • Design, build, and orchestrate machine learning, Generative AI, and agentic AI applications using foundational models via AWS Bedrock and SageMaker for automated billing, cost prediction, and document intelligence.
  • Design and build production-grade, scalable machine learning pipelines integrating optical character recognition, natural language processing, retrieval-augmented generation, and agentic decision-making workflows to process complex medical claims and unstructured data.
  • Establish end-to-end MLOps standards, automated model testing, and robust CI/CD pipelines for automated build, test, and release cycles for machine learning models and AI services.
  • Define, provision, and maintain secure cloud environments for AI/ML services using infrastructure as code tools such as AWS CDK or Terraform.
  • Use AI-assisted development tools such as GitHub Copilot or Kiro to accelerate code generation, optimize Python queries, auto-generate documentation, and maintain engineering standards.
  • Develop APIs and microservices to integrate machine learning and AI models into existing business applications.
  • Collaborate with product, technology, operations, and other cross-functional teams to align AI solutions with business goals.
  • Implement techniques to improve transparency in AI decision-making and ensure traceability and model explainability.
  • Ensure compliance with healthcare data regulations including HIPAA, HITRUST, and SOC 2.

About the Company

Wellrithms

AI-driven medical billing integrity for payors

WellRithms uses data science and AI-driven analysis together with physician-led review to check medical bills against clinical documentation, coding standards, and reimbursement benchmarks, helping payors pay accurate amounts. Bills are analyzed to flag potential issues, and physician reviewers validate findings, adjust misbillings, and ensure payments match documentation and coding rules. The company serves workers’ compensation payors with Comp42 and group health for self-funded employers, union trusts, brokers, and TPAs, offering industry-specific solutions rather than generic audits. Its aim is to reduce improper payments and lower costs for payors by ensuring reimbursements reflect true clinical documentation and coding.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
Portland, OR
Founded
2014

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