ML Engineer
About The Position
Freed is building AI-powered tools to reduce the administrative burden on healthcare professionals and give clinicians more time to focus on patient care. The Machine Learning Engineer will help develop, improve, and scale the AI systems powering Freed’s products. The role sits at the intersection of applied machine learning, software engineering, and product development. The engineer will take ML solutions from experimentation into production and work with engineers and cross-functional partners to create reliable, high-quality AI experiences for clinicians. It is suited to someone who enjoys complex technical problems, moving quickly, and creating tangible impact. The role offers the opportunity to take ownership, influence technical direction, and help shape AI products that make clinicians’ lives easier.
Requirements
- 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 Haves
- 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.
Responsibilities
- 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.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Education Level
No Education Listed
