AI/ML Engineer

Company: DOMA Technologies

Location: Washington, DC (Remote)

Type: Full-time

Remote: Yes

Posted: 2026-08-21

About this role

Description
At Commence, we’re the start of a new age of data-centric transformation, elevating health outcomes and powering better, more efficient process to program and patient health. We combine quality data-driven solutions that fuel answers, technology that advances performance, and clinical expertise that builds trust to create a more efficient path to quality care.


With human-centered, healthcare-relevant, and value-based solutions, we create new possibilities with data. We provide proof beyond the concept and performance beyond the scope with a focus on efficiencies that transform the lives of those we serve. With a culture driven by purpose, straightforward communication and clinical domain expertise, Commence cuts straight to better care.?


Requirements
The AI/ML Engineer builds, trains, and validates machine learning models that power clinical and operational data products across the healthcare ecosystem, and contributes hands-on to productionizing those models, including supporting document processing pipelines and AI/LLM-driven workflows.


The ideal candidate has solid hands-on ML experience, is comfortable working with healthcare datasets (EHRs, claims, FHIR), and can independently own well-scoped model development work while relying on senior engineers for system design, compliance, and architecture decisions.

  • Build, train, and validate machine learning models, including data ingestion, feature engineering, and model evaluation, against requirements defined by Senior Engineers or Data Scientists.
  • Contribute to productionizing machine learning and AI models into APIs, batch, and streaming workflows, under the direction of Senior AI/ML Engineers.
  • Support development of AI/LLM-driven workflows (e.g., document extraction, classification, summarization) using frameworks and orchestration patterns established by senior technical staff.
  • Assist in implementing Retrieval-Augmented Generation (RAG) components, embedding st...

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