Sr. Engineer, Machine Learning Operations

Role Overview

Sr. Engineer, Machine Learning Operations (MLOps) will work with cross-functional partners (biostatisticians, bioinformatics scientists, AI scientists, and software engineers) to deploy, operate, and scale machine learning solutions in production for cancer screening and precision oncology.

You will design, build, and maintain ML platforms and pipelines that support the full model lifecycle—ensuring reliability, security, and compliance.

Responsibilities

• Design, implement, and maintain end-to-end MLOps pipelines for training, validation, deployment, and monitoring.
• Build and operate scalable, secure ML infrastructure on cloud and container platforms (batch and real-time inference).
• Implement ML CI/CD workflows for data, model, and code, including automated testing and promotion across dev/staging/production.
• Establish model/data versioning, experiment tracking, and lineage for reproducibility, auditability, and governance.
• Develop monitoring/logging/alerting for model performance, data quality, drift, and system health; define and meet SLOs/SLAs.
• Translate experimental workflows into production-grade services integrated into internal/customer applications.
• Support Quality Management System policies and uphold company values.

Requirements

• 5 years of relevant job-related experience.
• Demonstrated experience with:
  • Python
  • At least one major ML framework (e.g., TensorFlow, PyTorch, scikit-learn)
  • Docker and Kubernetes
  • A major cloud platform supporting ML workloads (AWS, Azure, or GCP)
• Authorized to work in the country where the role is performed (no visa sponsorship).

Nice to Have

• 2+ years of experience in the life sciences domain (truncated in posting).