Senior ML Engineer - Healthcare AI (Hybrid)

athenahealth; Boston (MA); Hybride; USD 145 000 - 247 000; Plein temps

Avantages offerts par ce poste

Commuter support

Employee assistance programs

Tuition assistance

Employee resource groups

Collaborative workspaces

Dogs welcome

Flexible work arrangement

Résumé du poste

athenahealth, based in Boston, MA, is seeking a Senior Machine Learning Engineer to design, deploy, and optimize ML solutions that support healthcare products and analytics initiatives. This hybrid role partners with cross-functional teams to apply modern ML, data science, and software engineering practices across the full ML lifecycle.

You will identify opportunities, build production-grade ML services, create scalable data pipelines, and monitor model performance to drive continuous

Qualifications

• Bachelor’s or Master’s degree in Mathematics, Computer Science, Data Science, Statistics, or a related quantitative field, or equivalent practical experience.
• 4 to 6 years of professional hands-on experience developing, evaluating, and deploying machine learning models in production environments.
• Proficiency in Python, SQL, and Unix-based development environments.
• Experience building, testing, and maintaining production-grade machine learning services and workflows.
• Knowledge of machine learning fundamentals, statistical methods, model evaluation, and software engineering best practices.
• Familiarity with natural language processing, computer vision, or other applied machine learning techniques.
• Experience with deep learning models and complex neural network architectures is helpful.
• Experience training or fine-tuning large language models and generative artificial intelligence models is helpful.
• Experience with cloud platforms such as Amazon Web Services, including technologies such as Kubernetes, Kubeflow, or Elastic Kubernetes Service, is helpful.
• Strong communication skills, including the ability to communicate clearly in writing and in conversation with technical and non-technical audiences.

Responsabilités

• Identify opportunities to apply machine learning techniques to healthcare product and business problems and evaluate which approaches are most appropriate.
• Design and develop machine learning models and ML-based production services for client-facing and internal applications.
• Build scalable data pipelines, feature engineering workflows, and training datasets using structured and unstructured data.
• Deploy and maintain production machine learning services using cloud infrastructure and machine learning operations practices.
• Apply rigorous testing and validation methods to statistics, models, code, and production workflows to support quality and reliability.
• Follow and contribute to conventions and best practices for modeling, coding, architecture, and statistical methods.
• Collaborate effectively with colleagues across technical and non-technical functions to define requirements, communicate findings, and deliver solutions.
• Contribute to the development of internal tools, reusable frameworks, and team standards that improve the effectiveness of data science work.
• Use artificial intelligence tools to improve experimentation, coding, analysis, and workflow efficiency, while reviewing outputs carefully and applying sound judgment to technical decisions.
• Monitor model and service performance and improve solutions over time based on operational insights, changing requirements, and business impact.
• Support exploratory analyses, proofs of concept, and prototype development for emerging machine learning opportunities.
• Partner with platform and infrastructure teams to improve tooling for model training, deployment, observability, and reproducibility.
• Assist in establishing best practices for experiment tracking, model versioning, feature management, and continuous integration and continuous deployment.
• Prepare technical summaries, recommendations, and presentations for stakeholders across a range of technical backgrounds.
• Evaluate new tools, frameworks, and methodologies relevant to machine learning engineering, data science, and generative artificial intelligence.
• Participate in incident analysis and remediation efforts related to machine learning-enabled systems.
• Provide technical guidance and knowledge sharing to peers through collaboration, feedback, and documentation.
• Contribute to roadmap planning, estimation, and prioritization for machine learning and data science initiatives.

Connaissances

Python

SQL

Unix-based development environments

Production ML services

NLP / NLP techniques

Deep learning

Model evaluation

Cloud platforms

Kubernetes

Kubeflow

Communication skills

Formation

Bachelor’s degree in Mathematics, Computer Science, Data Science, Statistics, or related quantitative field

Master’s degree in Mathematics, Computer Science, Data Science, Statistics, or related field

Equivalent practical experience

Outils

Kubernetes

Kubeflow

Elastic Kubernetes Service

Description du poste

athenahealth, based in Boston, MA, is seeking a Senior Machine Learning Engineer to design, deploy, and optimize ML solutions that support healthcare products and analytics initiatives. This hybrid role partners with cross-functional teams to apply modern ML, data science, and software engineering practices across the full ML lifecycle.

You will identify opportunities, build production-grade ML services, create scalable data pipelines, and monitor model performance to drive continuous