ML Platform Engineer

Role Overview

This role will help enable algorithm development, model packaging, model registration, reproducible execution, and governed deployment workflows across imaging datasets, Integrated Data Products, and Analysis-Ready Datasets.

The role is focused on ML platform engineering, MLOps, model integration, and validation readiness rather than developing new clinical interpretation algorithms.

Key Responsibilities

• Build and support ML / MLOps capabilities within the Global Imaging Platform.
• Integrate ML models and workflows with GIP IDPs, ARDs, App Catalog, and model registry.
• Support SageMaker integration as the first connected algorithm development environment.
• Package algorithms for repeatable execution, versioning, lineage, and auditability.
• Implement model metadata, version control, experiment tracking, and reproducible build patterns.
• Support in-platform inferencing workflows for SageMaker-hosted or connected models.
• Work with data engineers, architects, and validation teams to ensure traceability from dataset to model output.
• Support CI/CD pipelines for ML model packaging, deployment, rollback, and promotion.
• Contribute to governance, observability, monitoring, and lifecycle controls for ML models.
• Support validation evidence generation, including documentation for GxP-ready workflows where applicable.
• Collaborate with architects, data scientists, platform engineers, and Genentech SMEs to align ML workflows with platform standards.
• Assist with onboarding future connected environments such as Vertex AI, Posit Workbench, and HPC.

Required Skills

• Strong hands-on experience with Python and ML engineering libraries.
• Experience with AWS SageMaker, model deployment, and endpoint / inference workflows.
• Experience with MLOps, model registry, experiment tracking, model versioning, and reproducibility.
• Familiarity with tools such as MLflow, Docker, GitHub, CI/CD pipelines, Kubernetes / EKS, or similar.
• Experience working with data pipelines, APIs, metadata, and lineage concepts.
• Understanding of model packaging, release management, rollback, and environment reproducibility.
• Ability to work with cross-functional teams across architecture, data engineering, validation, and product teams.
• Strong documentation skills for technical designs, implementation notes, and validation evidence.

Preferred Skills

• Experience in life sciences, clinical imaging, healthcare AI, or regulated data platforms.
• Familiarity with imaging formats and workflows such as DICOM, radiology, ophthalmology, or digital pathology.
• Experience with PyTorch, TensorFlow, or similar ML frameworks.
• Exposure to GxP, SaMD, validation, audit trail, traceability, or 21 CFR Part 11-aligned environments.
• Experience integrating ML models into enterprise platforms or application catalogs.
• Experience with cloud-native architecture and observability dashboards.

Expected Deliverables

• ML model packaging and registration workflows.
• SageMaker integration support for GIP algorithm framework.
• Reproducible ML execution patterns.
• Model metadata, lineage, and versioning implementation.
• ML lifecycle documentation and technical implementation notes.
• Support for validation evidence and audit-readiness documentation.
• Support for model deployment, monitoring, and rollback workflows.

Qualifications

• Bachelor s or Master s degree in Computer Science, Data Science, Engineering, Biomedical Engineering, or related field.
• 6+ years of experience in ML engineering, MLOps, or applied AI platform development.
• Prior experience supporting enterprise-scale ML platforms or regulated data environments is preferred.