Senior Machine Learning Engineer

Senior Machine Learning Engineer role for building and operating production machine learning systems for healthcare products and analytics initiatives.

Responsibilities

• Spot opportunities where machine learning can address healthcare product and business problems, and select appropriate solution approaches
• Design, develop, and optimize machine learning models and ML-based production services for both client-facing and internal applications
• Create 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 MLOps practices
• Apply testing and validation across statistics, models, code, and production workflows to support quality and reliability
• Follow and improve conventions and best practices for modeling, coding, architecture, and statistical methods
• Collaborate with technical and non-technical teams to define requirements, communicate results, and deliver solutions
• Develop internal tools, reusable frameworks, and team standards to improve data science effectiveness
• Use artificial intelligence tools to accelerate experimentation, coding, analysis, and workflow efficiency, with careful output review and sound technical judgment
• Monitor model and service performance and improve solutions over time based on operational insights, changing requirements, and business impact

Additional responsibilities

• Support exploratory analyses, proofs of concept, and prototypes for emerging machine learning opportunities
• Partner with platform and infrastructure teams to improve tooling for training, deployment, observability, and reproducibility
• Help establish best practices for experiment tracking, model versioning, feature management, and continuous integration and continuous deployment
• Produce technical summaries, recommendations, and presentations for stakeholders across technical backgrounds
• Evaluate new tools, frameworks, and methodologies for 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 a quantitative field (Mathematics, Computer Science, Data Science, Statistics, or related) or equivalent practical experience
• 4 to 6 years of professional, hands-on experience building, 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 AI models is helpful
• Experience with cloud platforms such as Amazon Web Services, including Kubernetes, Kubeflow, or Elastic Kubernetes Service, is helpful
• Strong communication skills for writing and conversation with both technical and non-technical audiences

Technologies

• Python, SQL, Unix
• Amazon Web Services (AWS)
• 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)

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