ML Engineer

About the role

• MACHINE LEARNING ENGINEER
• Location: San Francisco, CA
• Team: Engineering
• Employment Type: Full-time

About the Role

As a Machine Learning Engineer at Freed, you’ll work at the intersection of applied machine learning, software engineering, and product development.

You’ll help take ML solutions from experimentation into production, working closely with engineers and cross-functional partners to build reliable, high-quality AI experiences for clinicians.

This role is ideal for someone who enjoys solving complex technical problems, moving quickly, and seeing their work make a tangible impact.

What You’ll 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.

What We’re Looking for

• 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.