ashby
Senior Machine Learning Engineer
Paradigm Health
Remote, USA
Full-time
Senior · 5+ years experience
Remote
Discovered 1 weeks ago
PythonSQLCloudMachine LearningModel DeploymentMonitoring
Free
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Role Overview
As a Senior Machine Learning Engineer, you will take a leading role in designing and deploying sophisticated ML models, including GenAI and LLM-based solutions, that optimize clinical trial workflows and patient engagement. This position offers an opportunity to impact healthcare by developing state-of-the-art models that enhance trial design, accelerate patient recruitment, and improve overall trial efficiency. You will contribute to both high-level architecture decisions and hands-on implementation, driving the technology forward and influencing ML strategies across Paradigm.
Key Skills for This Role
PythonSQLCloudMachine LearningModel DeploymentMonitoring
Full Job Posting
Role Summary
As a Senior Machine Learning Engineer, you will take a leading role in designing and deploying sophisticated ML models, including GenAI and LLM-based solutions, that optimize clinical trial workflows and patient engagement. This position offers an opportunity to impact healthcare by developing state-of-the-art models that enhance trial design, accelerate patient recruitment, and improve overall trial efficiency. You will contribute to both high-level architecture decisions and hands-on implementation, driving the technology forward and influencing ML strategies across Paradigm.
Responsibilities
- Model Development & Deployment: Lead the development, testing, and deployment of ML models and pipelines, with a focus on scalability and integration into production systems.
- Advanced GenAI/LLM Applications: Design and refine GenAI/LLM-based models to streamline and automate clinical trial operations, from data gathering to real-time performance monitoring.
- Cross-Functional Collaboration: Partner with clinicians, informaticists, data scientists, and engineers to build solutions aligned with Paradigm’s mission and goals.
- Infrastructure & Performance Optimization: Drive improvements in model deployment infrastructure, develop monitoring tools, and refine model performance to ensure robust production-level reliability.
- Technical Leadership & Mentorship: Mentor junior ML engineers, contributing to team knowledge-sharing and establishing best practices for data science and machine learning.
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