Machine Learning Engineer

Machine Learning Engineer to support production AI by building the infrastructure and pipelines that deploy, serve, and monitor ML models for Clinical and Generative AI solutions.

Responsibilities

• Build and maintain scalable infrastructure and pipelines for model deployment and production inference (not model development)
• Collaborate with Data Scientists and Product Teams to turn model outputs into production-ready services
• Support scalability, performance, and reliability of inference workflows
• Design and implement data pipelines that enable ML workflows
• Participate in code reviews to maintain quality and engineering best practices
• Apply established software design patterns and contribute to architectural discussions
• Find and implement practical improvements to ML enablement processes
• Contribute to an AI Development Platform that supports enterprise adoption of AI capabilities
• Work with Data Engineering and DevOps to build processing workflows and pipelines
• Partner with product and engineering teams to identify reusable datasets, components, and infrastructure
• Balance cost, time, and technical capability when delivering solutions
• Collaborate with senior team members, learn continuously, and share knowledge with peers
• Contribute to assigned workstreams to help ensure successful, timely completion
• Work independently on well-defined problems; escalate ambiguous or high-impact decisions as needed

Requirements

• 3-5 years of experience in machine learning engineering or a related role
• Strong programming skills in Python; additional languages are a plus
• Experience building scalable data processing and ML-enabling applications
• Basic knowledge of Spark for data processing
• Understanding of sound engineering practices, including testing and CI/CD
• Working knowledge of AI/ML concepts with some hands-on experience supporting models in production environments
• Familiarity with ML stacks including Databricks, Delta Tables, AWS (EC2, S3, SageMaker), and containerization tools such as Docker
• Basic knowledge of data engineering (SQL, NoSQL, Big Data), cloud architecture, and Agile methodologies
• Good analytical and problem-solving skills; able to adapt to evolving technologies
• Good communication skills for collaboration with data scientists, engineers, and stakeholders

Location & Work Model

• Portland, ME (hybrid)
• In-office requirement: 2 days per week

Compensation

• USD 115,000 per year (base salary range starting at this level based on experience)
• Opportunity for an annual cash bonus

Benefits

• Health / Dental / Vision benefits Day-One
• 401(k) match: 5%
• Additional benefits including (but not limited to) financial support, pet insurance, mental health resources, volunteer paid days off, employee stock program, foundation donation matching, and more

Technologies

• Python
• Spark
• Databricks
• Delta Tables
• AWS: EC2, S3, SageMaker
• Docker
• SQL
• NoSQL
• Big Data
• CI/CD
• Agile methodologies