Machine Learning Engineer II

Sorry, this job was removed at 08:16 p.m. (CST) on Friday, Aug 29, 2025
Boston, MA
In-Office
Software
The Role

Machine Learning Engineer II

CodaMetrix is revolutionizing Revenue Cycle Management with its AI-powered autonomous coding solution, a multi-specialty AI-platform that translates clinical information into accurate sets of medical codes. CodaMetrix’s  autonomous coding supports improved patient care and drives efficiency under fee-for-service and value-based care models.

We are passionate about getting doctors away from the keyboard and back to clinical care.
The Team

At CodaMetrix, the Machine Learning and AI team is responsible for the invention, analysis, and deployment of new machine learning techniques using healthcare data to improve administrative and clinical medicine.
The Role

As a Machine Learning Engineer II with CodaMetrix you will help with translating proof-of-concept ideas to product grade solutions. You will work closely with data scientists, product owners, and backend engineers to gather requirements and understand performance criteria to deliver solutions that bring our AI-driven robust and scalable products to market. The Machine Learning Engineer II reports to the Director of Architecture.

Responsibilities
  • Participate in all team activities, including agile ceremonies and reviews of other engineers designs and code.
  • Build and maintain the ML infrastructure used by the ML Team and used in production.
  • Process, transform, and enrich incoming data in order to be compatible with our ML infrastructure and for use by the ML Team.
  • Develop pipelines and build customer models in support of production.
  • Establish processes for managing the transitions from proof-of-concept to production.
  • Promote best practice software development principles.
  • Collaborate with machine learning, engineering, product development, professional services, DevOps, Data Intelligence, and coding quality assessment teams, for development & deployment of new machine learning techniques and follow deployments, tracking issues, and successes.
 Requirements  
  • Master's degree in software engineering, computer science or a related technical field with significant course or project work in machine learning, artificial intelligence, or data science. Candidates with a bachelor’s degree with 4 or more years professional software development experience will be considered.
  • Minimum of two years experience in professional software development.
  • Fluent with software development best practices, including version control, documentation, testing and CI/CD (Jenkins, Github Actions, Terraform)
  • Extensive experience with machine learning approaches and an understanding of the analysis and testing processes of machine learning algorithms. Experience with natural language processing (NLP) is a plus.
  • Must have experience with cloud computing platforms. Experience with AWS (S3, EC2, etc) is a plus.
  • Experience with SQL and NoSQL Databases. Experience with SparkSQL is a plus.
  • Proficiency with Python. Experience with Java and/or Scala is a plus.
  • Proficiency in working within a Linux ecosystem and extensive experience with Docker.
  • Experience with Spark, particularly Databricks, is a plus.
 

Beneficial Experience

  • Experience working with healthcare data and knowledge of US healthcare systems
  • Familiarity with deep learning approaches and Large Language Models.
  • Familiarity with big data and distributed computing approaches.
  • Publications or contributions to the machine learning community or open-source healthcare projects.
  • Experience working in a startup / fast paced environment

Location: Boston, MA/Remote - Hybrid
 

Job Type: regular, full-time

Salary: $130,000 +


 

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The Company
HQ: Boston, MA
119 Employees
Year Founded: 2019

What We Do

CodaMetrix's cutting-edge, multi-specialty autonomous medical coding platform leverages AI to continuously learn from and act upon the clinical evidence in the EHR. We autonomously translate clinical notes into billing codes that satisfy coding requirements, ensuring claims consistently represent the unique and complete episode of care, reducing human coding workload, while improving the efficiency and optimizing the quality of medical coding.

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