Machine Learning Engineer

Manulife•Boston, MA

5d•$90,160 - $167,440•Hybrid

About The Position

John Hancock Life Insurance Company (USA) is seeking a Machine Learning Engineer to develop, build, and maintain scalable data pipelines supporting underwriting, analytics, and machine learning systems. The role involves developing and deploying backend services and microservices for data ingestion, retrieval, and ML inference workflows. Responsibilities include deploying, monitoring, and maintaining machine learning pipelines and backend services in production, conducting root-cause analysis of issues, and collaborating with cross-functional teams to translate business requirements into technical solutions. The position also supports production releases and large-scale batch processing of underwriting workflows.

Requirements

Master’s degree or foreign equivalent in Data Science, Information Studies, Information Science, Computer Science, Machine Learning, or a closely related field
3 years of Python programming experience for backend systems, automation, or machine learning workloads
3 years of experience using SQL or NoSQL databases including PostgreSQL or MongoDB for data modeling, querying, or pipeline integration
3 years of experience developing or deploying machine learning models using PyTorch or TensorFlow
3 years of experience building or maintaining backend APIs or microservices using FastAPI or Flask
2 years of experience deploying ML workflows or backend services using AWS or Azure
2 years of experience using containerization and orchestration tools including Docker and Kubernetes
2 years of experience using GitHub Actions or TeamCity for Continuous Integration/ Continuous Deployment (CI/CD)
2 years of experience designing distributed or scalable systems including microservices, load balancing or distributed processing

Responsibilities

Develop, build and maintain scalable data pipelines supporting underwriting, analytics, and machine learning systems
Develop and deploy backend services and microservices to support data ingestion, retrieval, and ML inference workflows
Deploy, monitor, and maintain machine learning pipelines and backend services in production environments
Conduct root‑cause analysis of data, model, and pipeline issues and implement corrective solutions
Collaborate with data scientists, underwriters, and engineering teams to translate business requirements into technical solutions
Support production releases and large‑scale batch processing of underwriting workflows