Remote ML Platform Engineer II

Brooksource

St. Louis (MO)

On-site

USD 68,880 - 75,768

Full time

14 days+

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Benefits offered by this job

401(k) matching plan
Medical, dental & vision plans
Relationship-driven process to find你的

Job summary

Brooksource is hiring a Machine Learning Engineer II to support an enterprise Predictive Modeling Organization within the Enterprise AI Platform Team for a Fortune 50 healthcare client. This remote (U.S.) position focuses on building scalable ML frameworks and reusable platform capabilities used by dozens of data science teams.

You will develop production-quality Python applications, improve platform performance, and collaborate with researchers and engineers to productionize AI capabilities at

Qualifications

  • 1–3 years of experience in Software Engineering, ML Engineering, or related technical discipline.

Responsibilities

  • Design and develop scalable machine learning frameworks and reusable platform capabilities for enterprise AI initiatives.
  • Build production-quality Python applications and tooling for deep learning model development and deployment.
  • Improve scalability and performance of the ML platform across the organization.
  • Collaborate with researchers, data scientists, and software engineers to productionize AI capabilities.
  • Support model monitoring, platform reliability, and continuous improvement of production ML systems.
  • Expand the platform to support new healthcare data sources and predictive modeling use cases.

Skills

Python
Software engineering
Deep learning
PyTorch
PyTorch Lightning
Apache Spark

Tools

MLOps
Transformer architectures

Job description

Brooksource is hiring a Machine Learning Engineer II to support an enterprise Predictive Modeling Organization within the Enterprise AI Platform Team for a Fortune 50 healthcare client. This remote (U.S.) position focuses on building scalable ML frameworks and reusable platform capabilities used by dozens of data science teams.

You will develop production-quality Python applications, improve platform performance, and collaborate with researchers and engineers to productionize AI capabilities at

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