As a Machine Learning Engineer II, you'll develop and maintain ML infrastructure, create customer models, and collaborate with various teams to enhance AI-driven products.
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.
- 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 +
Top Skills
AWS
Databricks
Docker
Github Actions
Java
Jenkins
NoSQL
Python
Scala
Spark
SQL
Terraform
CodaMetrix Boston, Massachusetts, USA Office
Boston, MA, United States, 02116
Similar Jobs
Artificial Intelligence • Fintech • Machine Learning • Natural Language Processing • Software • Generative AI
Develop and scale production-grade RAG systems focusing on retrieval models and LLM orchestration. Collaborate with teams for seamless ML implementation.
Top Skills:
AirflowAWSDockerHuggingfaceJenkinsKubernetesLabelboxLangchainLlamaindexOpensearchPgvectorPythonPyTorchTransformersWeights & Biases
Fitness • Hardware • Healthtech • Sports • Wearables
As a Machine Learning Engineer II, you'll architect ML systems, optimize models, and deploy solutions while collaborating with engineering teams to enhance health metrics.
Top Skills:
AirflowAWSDockerGCPGitKubernetesMlflowNumpyPandasPythonPyTorchScikit-LearnScipyTensorFlow
eCommerce • Healthtech • Pet • Retail • Pharmaceutical
The Machine Learning Engineer II will develop and deploy machine learning models, conduct exploratory data analysis, and collaborate across teams to enhance product discovery for Chewy customers.
Top Skills:
AWSGoogle Cloud PlatformMachine LearningPyTorchTensorFlow
What you need to know about the Boston Tech Scene
Boston is a powerhouse for technology innovation thanks to world-class research universities like MIT and Harvard and a robust pipeline of venture capital investment. Host to the first telephone call and one of the first general-purpose computers ever put into use, Boston is now a hub for biotechnology, robotics and artificial intelligence — though it’s also home to several B2B software giants. So it’s no surprise that the city consistently ranks among the greatest startup ecosystems in the world.
Key Facts About Boston Tech
- Number of Tech Workers: 269,000; 9.4% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Thermo Fisher Scientific, Toast, Klaviyo, HubSpot, DraftKings
- Key Industries: Artificial intelligence, biotechnology, robotics, software, aerospace
- Funding Landscape: $15.7 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Summit Partners, Volition Capital, Bain Capital Ventures, MassVentures, Highland Capital Partners
- Research Centers and Universities: MIT, Harvard University, Boston College, Tufts University, Boston University, Northeastern University, Smithsonian Astrophysical Observatory, National Bureau of Economic Research, Broad Institute, Lowell Center for Space Science & Technology, National Emerging Infectious Diseases Laboratories
