Senior Machine Learning Engineer

Senior Machine Learning Engineer role focused on building and improving production ML systems for athenahealth healthcare products and analytics.

Responsibilities

• Spot opportunities for applying machine learning to healthcare product and business problems, and select appropriate approaches
• Design, build, and deliver machine learning models and ML production services for client-facing and internal applications
• Create scalable data pipelines, feature engineering workflows, and training datasets using structured and unstructured data
• Deploy, operate, and maintain production machine learning services using cloud infrastructure and machine learning operations (MLOps) practices
• Apply rigorous testing and validation for statistics, models, code, and production workflows to support quality and reliability
• Follow and contribute to best practices for modeling, coding, architecture, and statistical methods
• Collaborate across technical and non-technical partners to define requirements, share findings, and deliver solutions
• Develop internal tools, reusable frameworks, and team standards to increase the effectiveness of data science work
• Use artificial intelligence tools to speed up experimentation, coding, analysis, and workflow efficiency, with careful review and sound technical judgment
• Monitor model and service performance, then improve solutions over time based on operational insights, changing requirements, and business impact
• Support exploratory analysis, 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
• Help establish best practices for experiment tracking, model versioning, feature management, and CI/CD
• Produce technical summaries, recommendations, and presentations for stakeholders with varied 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 for machine learning-enabled systems
• Provide technical guidance and knowledge sharing through collaboration, feedback, and documentation
• Contribute to roadmap planning, estimation, and prioritization for machine learning and data science initiatives

Requirements

• 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 hands-on professional 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
• Helpful: experience with cloud platforms such as Amazon Web Services, including Kubernetes, Kubeflow, or Elastic Kubernetes Service
• Strong communication skills, including clear writing and conversation with technical and non-technical audiences

Technologies

• Python, SQL, Unix
• Amazon Web Services, Kubernetes, Kubeflow, Elastic Kubernetes Service
• Natural language processing, computer vision
• Deep learning models, large language models, generative artificial intelligence
• Artificial intelligence tools
• Machine learning operations (MLOps)
• Feature engineering
• Continuous integration and continuous deployment

Benefits

• Annual discretionary bonus plan
• Variable compensation plan
• Equity plans
• Health and financial benefits
• Commuter support
• Employee assistance programs
• Tuition assistance
• Employee resource groups
• Collaborative workspaces
• Full-time flexibility with consistent communication and digital collaboration tools
• Sponsorship of events throughout the year, including book clubs, external speakers, and hackathons

Location: Boston, MA (hybrid)

Salary: USD 145,000 - 247,000 per year

Minimum experience: 4 years

Education: Bachelor’s or Master’s degree in a quantitative field (or equivalent practical experience)