Freed

ML Engineer

San Francisco , Remote. Posted 15 days ago

The role

You will develop, evaluate, and improve machine learning models and AI-powered product capabilities. You will build and maintain production ML pipelines, services, and supporting infrastructure.

What you will do

  • Develop, evaluate, and improve machine learning models and AI-powered product capabilities.
  • Build and maintain production ML pipelines, services, and supporting infrastructure.
  • Design experiments and evaluation frameworks to measure model quality, reliability, and performance.
  • Collaborate with product and engineering teams to translate user needs into practical technical solutions.
  • Improve the scalability, efficiency, and reliability of ML systems in production.
  • Investigate model failures, identify opportunities for improvement, and iterate using real-world feedback.
  • Contribute to technical decisions and engineering best practices across the ML lifecycle.

Required qualifications

  • Strong software engineering fundamentals and experience building production systems.
  • Hands-on experience developing and deploying machine learning solutions.
  • Proficiency in Python and familiarity with modern ML frameworks and tooling.
  • Experience evaluating model performance and improving systems through experimentation.
  • Understanding of ML infrastructure, deployment, monitoring, and production reliability.
  • Ability to work independently, communicate clearly, and collaborate across disciplines.
  • Comfort operating in a fast-moving environment with evolving technical and product requirements.

Nice to have

  • Experience with large language models, generative AI, or speech-related ML systems.
  • Experience with model evaluation, fine-tuning, or inference optimization.
  • Familiarity with healthcare technology or other high-reliability product environments.
  • Experience building ML products in an early-stage startup.

About the company

Freed is building AI-powered tools that reduce the administrative burden on healthcare professionals, giving clinicians more time to focus on patient care.