Machine Learning Engineer

eCom Solutions, Inc.; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

eCom Solutions, Inc. is seeking a remote ML Engineer to help enable algorithm development and reproducible deployment workflows across imaging data products.

The role focuses on ML platform engineering, MLOps, model integration, and governance, rather than developing new clinical interpretation algorithms. You'll collaborate with data scientists and engineers to ensure traceability from dataset to model output and support onboarding to Vertex AI, Posit Workbench, and HPC.

Qualifications

• Bachelor’s or Master’s degree in CS, DS, Engineering, or related field.
• Minimum 4 years of ML engineering, MLOps, or applied AI platform experience.
• Experience supporting enterprise-scale ML platforms or regulated data environments preferred.
• Strong proficiency in Python and ML libraries.
• Experience with imaging data workflows and regulated environments is a plus.

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.
• Collaborate 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 and audit-readiness documentation.
• Collaborate with Genentech SMEs to align ML workflows with platform standards.
• Assist onboarding connected environments such as Vertex AI, Posit Workbench, and HPC.

Skills

Python

AWS SageMaker

MLOps

MLflow

Docker

GitHub

CI/CD

Kubernetes / EKS

Data pipelines

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Biomedical Engineering, or related field

Tools

MLflow

Docker

GitHub Actions

Kubernetes

Job description

Job Title: ML Engineer

Location: Remote

Duration: Long Term

Project: Global Imaging Platform Design & Build

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.
• 4 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.