Hands-On ML Engineer for Medical Imaging AI

Avara Software LLC; Saint Petersburg (FL); On-site; USD 110,000 - 170,000; Full time

Job summary

Avara Software LLC is seeking a creative and hands-on ML engineer to build and iterate on ML models powering our AI-driven radiology tools. You will own training, inference, and product integration while collaborating directly with the technical founder and customers to obsessively improve model performance.

You will develop and train models, deploy them to production, and monitor performance, shaping UX for end consumers and ensuring robust model outcomes.

Qualifications

• 2+ years of machine learning engineering experience.
• Bachelor's degree preferred but not required with a proven track record.
• Strong proficiency in Python and PyTorch.
• Experience training computer vision models.
• Experience with training LLMs, VLMs, and transformers is a plus.
• Experience with ETL tools (Apache Beam, Modal, AWS Glue).
• Experience with medical imaging or healthcare data is a plus.
• Extremely optimistic and loves challenges.
• Thrives with ambiguous problems.
• Willing to work weekends with the founders.

Responsibilities

• Develop and train ML models for medical imaging analysis.
• Collaborate with our technical founder to build training and inference frameworks.
• Curate and obsessively perfect UX for end consumers.
• Deploy models to production and monitor performance.

Skills

Python

PyTorch

Medical imaging domain knowledge

Education

Bachelor's degree in a relevant field

Tools

Apache Beam

Modal

AWS Glue

Transformers

Job description

Avara Software LLC is seeking a creative and hands-on ML engineer to build and iterate on ML models powering our AI-driven radiology tools. You will own training, inference, and product integration while collaborating directly with the technical founder and customers to obsessively improve model performance.

You will develop and train models, deploy them to production, and monitor performance, shaping UX for end consumers and ensuring robust model outcomes.