Staff Data Engineer, Applied Machine Learning

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Requirements:

• Proven experience in a leadership role driving ML system development and optimization, preferably in healthcare or related fields.
• Demonstrated expertise in training ML models and building robust training pipelines for healthcare applications.
• Strong understanding of machine learning frameworks such as TensorFlow, PyTorch, or similar, with applications in healthcare.
• Proficient in programming languages like Python or Go, with the ability to write efficient, clean, and maintainable code for healthcare systems.
• Excellent written and verbal communication skills, with the ability to convey technical concepts to both technical and non-technical audiences in healthcare settings.
• A track record of delivering impactful machine learning solutions that have been successfully deployed in real-world healthcare applications.
• Familiarity with healthcare data privacy regulations and best practices for handling sensitive medical information.

Responsibilities:

• Lead the team in architecting, building, and optimizing ML systems to deliver high-quality, real-world results in healthcare settings.
• Design and implement robust training pipelines for machine learning models, ensuring efficiency and scalability for healthcare data.
• Fine-tune ML models to meet specific healthcare needs and optimize their performance for various medical applications.
• Develop and implement feedback mechanisms to continuously improve the accuracy and effectiveness of ML in healthcare contexts.
• Collaborate with cross-functional teams to understand healthcare business requirements and translate them into actionable ML solutions.
• Stay up-to-date with the latest advancements in machine learning and healthcare technology, implementing best practices to enhance our ML infrastructure.
• Coach and mentor junior data engineers, fostering a culture of continuous learning and growth within the Lightbox Health team.
• Communicate complex technical concepts and findings to non-technical stakeholders in a clear and concise manner, particularly in healthcare contexts.