Sr. Engineer, Machine Learning Operations

Help us change lives

At Exact Sciences , we’re helping change how the world prevents, detects and guides treatment for cancer. We give patients and clinicians the clarity needed to make confident decisions when they matter most. Join our team to find a purpose-driven career, an inclusive culture, and robust benefits to support your life while you’re working to help others.

Position Overview

The Sr. Engineer, Machine Learning Operations, with minimal guidance, works independently and with cross‑functional partners—including biostatisticians, bioinformatics scientists, AI scientists, and software engineers—to deploy, operate, and scale machine learning solutions in production for advanced cancer screening and precision oncology applications. The role designs, builds, and maintains robust ML platforms and pipelines that ensure reliability, security, and compliance across the full model lifecycle—from data ingestion, model training, versioning and evaluation, through deployment, monitoring, and continuous improvement. This role serves as a key resource, applying in‑depth practical knowledge of ML Operations, software engineering, and cloud infrastructure to solve complex problems across multiple projects, ensuring AI/ML models are production-ready, observable, and aligned with the company's mission to help eradicate cancer.

Essential Duties

Include, but are not limited to, the following:

• Designs, implements, and maintains end‑to‑end MLOps pipelines for training, validation, deployment, and monitoring of ML and AI models used in cancer screening and precision oncology solutions.
• Builds and operates scalable, secure ML infrastructure on cloud and container platforms (e.g., AWS/Azure/GCP, Docker, Kubernetes) to support batch and real‑time inference workloads.
• Implements CI/CD workflows for ML (data, model, and code), including automated testing, packaging, and promotion of models across development, staging, and production environments.
• Establishes and manages model and data versioning, experiment tracking, and lineage to ensure reproducibility, auditability, and effective model governance.
• Develops and maintains monitoring, logging, and alerting for model performance, data quality, drift, and system health, defining and meeting SLOs/SLAs for critical ML services.
• Collaborates with data scientists, bioinformatics and biostatistics partners, and software/platform engineering teams to translate experimental workflows into production‑grade services integrated into customer‑facing and internal applications.
• Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork.
• Support and comply with the company’s Quality Management System policies and procedures.
• Maintain regular and reliable attendance.
• Ability to act with an inclusion mindset and model these