Senior ML Engineer: Applied Research & Model Deployment

PathAI; Town of Vernon (NY); On-site; USD 131,000 - 200,000; Full time

Job summary

PathAI is seeking Machine Learning Engineers (Applied Research & Model Development) to tackle unique ML challenges to advance medicine and improve patient care. You will design, develop, and deploy models for research and product development, collaborating across biomedical data science, product, translational research, MLOps, and platform teams.

You will work with leading innovators in AI and medicine, publish scientific work, and contribute to ML pipelines and governance.

Qualifications

• Master’s degree with 5+ years or PhD with 3+ years in ML/CS/DS/Statistics/Applied Math or related
• Proven track record deploying ML models to production or research
• Strong Python proficiency and data pipeline development

Responsibilities

• Design, develop, and deploy machine learning models for research and product development projects
• Collaborate cross-functionally to translate biological and clinical requirements into ML solutions
• Contribute to experimental design, analysis, and reporting
• Publish and present scientific work and contribute to abstracts/manuscripts/conference materials
• Improve ML pipelines and infrastructure with MLOps and platform teams
• Participate in design reviews, journal clubs, and governance activities

Skills

Python

ML frameworks

Data pipelines

Independent work

Education

Master's degree in ML/CS/Data Science/Statistics/Applied Math

PhD in ML/CS/Data Science/Statistics/Applied Math

Tools

TensorFlow

PyTorch

scikit-learn

Job description

PathAI is seeking Machine Learning Engineers (Applied Research & Model Development) to tackle unique ML challenges to advance medicine and improve patient care. You will design, develop, and deploy models for research and product development, collaborating across biomedical data science, product, translational research, MLOps, and platform teams.

You will work with leading innovators in AI and medicine, publish scientific work, and contribute to ML pipelines and governance.