Senior Machine Learning Engineer

Athenahealth; Boston (MA); Hybrid; USD 145,000 - 247,000; Full time

Benefits offered by this job

Annual discretionary bonus plan

Equity plans

Health and financial benefits

Commuter support

Tuition assistance

Job summary

Athenahealth in Boston, MA is seeking a Senior Machine Learning Engineer to design, develop, deploy, and optimize ML solutions for healthcare products and analytics initiatives.

You will collaborate across teams through the ML lifecycle, improve model performance, and build scalable data pipelines and production services using cloud infrastructure and MLOps practices. Strong communication is essential in this hybrid role.

Qualifications

• Bachelor’s or Master’s in Mathematics, CS, DS, or Statistics.
• 4–6 years of hands-on ML model development & deployment.
• Proficiency with Python, SQL and Unix environments.
• Experience with cloud platforms (AWS) and ML tooling.

Responsibilities

• Assess opportunities to apply ML to healthcare problems and select methods.
• Design, develop, and deploy ML models and production services.
• Build scalable data pipelines and training datasets.
• Deploy and maintain ML services using cloud infrastructure and MLOps.
• Monitor performance and improve models over time.
• Collaborate across teams to define requirements and communicate findings.

Skills

Strong communication

Problem solving

Education

Bachelor’s/Master’s in a quantitative field

Tools

Python

SQL

Unix

AWS

Kubernetes

Kubeflow

Elastic Kubernetes Service

Job description

Athenahealth is seeking a Senior Machine Learning Engineer in Boston, MA (hybrid) to design, develop, deploy, and optimize machine learning solutions for healthcare products and analytics initiatives. This role involves collaborating across teams throughout the ML lifecycle and improving model performance over time.

Key Responsibilities

• Assess opportunities to apply machine learning techniques to healthcare product and business problems and determine the most appropriate approaches.
• 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.
• Use rigorous testing and validation methods across statistics, models, code, and production workflows to support quality and reliability.
• Adhere to and contribute to conventions and best practices for modeling, coding, architecture, and statistical methods.
• Partner with technical and non-technical colleagues to define requirements, communicate findings, and deliver solutions.
• Develop internal tools, reusable frameworks, and team standards that improve the effectiveness of data science work.
• Apply artificial intelligence tools to improve experimentation, coding, analysis, and workflow efficiency, while reviewing outputs and applying sound 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 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 continuous integration and continuous deployment.
• Create technical summaries, recommendations, and presentations for stakeholders across a range of technical backgrounds.
• Evaluate new tools, frameworks, and methodologies related 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 a quantitative field such as Mathematics, Computer Science, Data Science, or Statistics (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 Kubernetes, Kubeflow, or Elastic Kubernetes Service, is helpful.
• Strong communication skills, including clear written and verbal communication 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.

Location and Compensation

• Location: Boston, MA (hybrid)
• Salary: USD 145,000 - 247,000 per year

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)