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# Apptad-Machine Learning Engineer (MLE)

- **Company:** Apptad Inc.
- **Location:** San Francisco, CA, United States
- **Contract:** Contract
- **Skills:** Amazon Web Services, Data Analysis, Cloud Computing, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Transformation, Machine Learning, Cloud Platform System, Feature Engineering, Data Ingestion, Information Technology, Machine Learning Operations
- **Published:** November 12, 2026
- **Apply:** https://www.dice.com/job-detail/a2b71d7e-08f9-4b62-84f0-e72aa916f4fb

## About the Role

Proven track record as an MLE with experience productionizing models (rather than just DS/Analytics).

Cloud Platform

Expert knowledge of AWS (SageMaker, Lambda, Glue) for ML applications.

Data Engineering

Strong background in data engineering, ETL design, and data modeling.

Domain Knowledge

Experience in the Healthcare or Life Sciences sector.

Compliance

Understanding of regulatory experience and working within regulated data environments.

Education & Experience

 * Bachelor's degree in Business Administration, Information Technology, or a related field.

## Description

We are seeking a production-focused Machine Learning Engineer (MLE). Unlike a traditional Data Science role, this position prioritizes the engineering, deployment, and scalability of machine learning systems. You will be responsible for moving models from research to production, ensuring they are robust, integrated into our cloud infrastructure, and compliant with industry standards., * MLOps on AWS: Lead the end-to-end MLOps lifecycle within the AWS ecosystem, with a focus on CI/CD for machine learning and automated model monitoring.

 * Infrastructure & Data Modeling: Design and implement scalable infrastructure architecture, including complex data models specifically structured for ML workloads.
 * ML Pipelines: Build and maintain automated pipelines to handle data ingestion, preprocessing, training, and deployment at scale.
 * Feature Engineering: Develop and optimize sophisticated feature engineering workflows to enhance model accuracy and operational efficiency.
 * Regulatory & Data Engineering: Bridge the gap between data engineering and model deployment while adhering to strict regulatory requirements inherent to the life sciences industry.