AI/ML Engineer

About the Company
Commence delivers data-driven healthcare solutions designed to improve health outcomes and make patient and program processes more efficient. The company combines quality data, advanced technology, and clinical expertise to support better care.
With a focus on human-centered, healthcare-relevant, and value-based solutions, Commence helps organizations use data to improve performance and healthcare delivery. Its culture emphasizes purpose, straightforward communication, and clinical domain expertise.
About the Role
Commence is seeking an AI/ML Engineer to build, train, and validate machine learning models that support clinical and operational data products across the healthcare ecosystem.
The role involves hands-on model development and production support, including document processing pipelines and AI/LLM-driven workflows. The ideal candidate will bring practical machine learning experience, familiarity with healthcare datasets, and the ability to independently manage well-defined development tasks while working with senior engineers on system design,
architecture, and compliance decisions.
Key Responsibilities
Build, train, and validate machine learning models, including data ingestion, feature engineering, and model evaluation, based on requirements from Senior Engineers or Data Scientists.
Help productionize machine learning and AI models through APIs, batch processing, and streaming workflows under the direction of Senior AI/ML Engineers.
Support AI/LLM-driven workflows, including document extraction, classification, and summarization, using frameworks and orchestration patterns established by senior technical staff.
Assist in implementing Retrieval-Augmented Generation (RAG) components, embedding strategies, and vector-based retrieval within architectures designed by senior engineers.
Apply established MLOps practices, including CI/CD pipelines, model versioning, and retraining workflows, following existing team standards.
Work with Databricks services, including Workflows, Delta Lake, and Unity Catalog, and AWS services such as S3, Lambda, and Bedrock to support model training and inference.
Monitor deployed model performance, including latency, throughput, and accuracy drift, and troubleshoot issues while escalating architecture-level problems to Senior Engineers.
Use and maintain existing monitoring, alerting, and observability tools for assigned models.
Collaborate with Data Engineers, Data Scientists, and Software Engineers to deliver components of larger AI/ML systems.
Follow established practices to ensure AI/ML work complies with HIPAA, 42 CFR Part 2, FedRAMP Moderate, and other applicable regulatory frameworks.
Apply secure data handling practices, including encryption, access controls, and protection of sensitive healthcare data.
Stay informed about emerging AI/ML tools and techniques and bring relevant options to the team for evaluation.
Document model development, testing, and validation activities to support reproducibility and audit readiness.
Required Qualifications
Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
3–5 years of experience in machine learning engineering, data science, or software engineering.
Strong proficiency in Python and SQL, with experience building production-grade systems.
Practical experience with machine learning libraries such as Scikit-learn, TensorFlow, or PyTorch.
Experience deploying machine learning models in production environments through batch or API-based workflows, with support from senior engineers on system design.
Exposure to AI/LLM platforms such as AWS Bedrock, Anthropic, LangChain, or Databricks Agent frameworks.
Familiarity with distributed data processing frameworks such as PySpark, Databricks, or AWS EMR.
Experience with ML lifecycle tools such as MLflow, model registries, and monitoring frameworks.
Experience building APIs or services for model inference using tools such as FastAPI or Flask.
Solid understanding of software engineering principles and their application to machine learning workflows.
Experience working with healthcare datasets such as EHRs, claims, FHIR, or HL7.
Strong problem-solving skills and attention to detail.
Strong communication and collaboration skills when working with technical and non-technical teams.
Preferred Qualifications
Exposure to vector databases, embeddings, or RAG components.
Experience with OCR and document AI tools such as AWS Textract or DBX OCR.
Experience with containerization and orchestration tools such as Docker and Kubernetes.
Familiarity with healthcare data governance and security frameworks such as NIST or HITRUST.
Experience supporting federal healthcare programs or agencies such as CMS, VA, or DoD.
Knowledge of healthcare delivery systems, quality measurement programs, or policy frameworks.
Employment Details
Employment Type: Full-time
Work Arrangement: Remote
Work Environment and Physical Requirements
This is a remote position. The work environment is climate-controlled. Employees must be able to sit, read, work on a computer, and view a computer screen for extended periods. Occasional physical activities may include standing, walking, using hands and fingers, kneeling, or crouching.
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of the role.
Equal Employment Opportunity
Commence is an equal employment opportunity employer. All personnel processes are merit-based and conducted without discrimination based on race, color, religion, sex, sexual orientation, gender identity, marital status, age, disability, national or ethnic origin, military or veteran status, or any other characteristic protected by applicable law.
Commence is committed to providing equal employment opportunities to all applicants, including individuals with disabilities. Applicants who require reasonable accommodation during the application process may contact Human Resources at (757) 306-4920 or [email protected].
Unless requesting an accommodation, all applications must be submitted through the company's online application system.