AI/Machine Learning Engineer, Citizen Health

• You are a builder and problem solver at heart who loves working in fast-paced, innovative environments
• You have experience working in consumer technology companies and have also worked on products that deal with complex data and systems
• You love start-ups and can operate autonomously knowing that collaboration is the key to your success
• 5+ years of experience in machine learning engineering, with a focus on production ML systems
• Strong proficiency in Python and AI/LLM frameworks (LangChain, LangGraph, AutoGen, CrewAI)
• Experience designing and implementing production-grade ML pipelines
• Proven track record of deploying ML/LLM solutions in production environments
• Strong understanding of ML fundamentals, including deep learning, NLP, and statistical modeling
• Experience with ML ops tools and best practices
• Proficiency in working with large-scale datasets and distributed computing
• Strong software engineering skills and coding best practices
• Degree in Computer Science or equivalent degree

Desirable

• Citizen Health is seeking AI/Machine Learning Engineers to lead the development and deployment of cutting-edge AI solutions that power our patient-centric platform
• This role is crucial in designing and implementing sophisticated machine learning models that transform complex health data into actionable insights for patients, healthcare providers, and researchers
• As a key technical leader, you'll be at the forefront of applying advanced machine learning techniques to solve challenging problems in rare disease research and patient care
• Your work will be instrumental in developing AI-driven solutions that improve disease understanding, treatment recommendations, and patient outcomes
• Design and implement end-to-end machine learning solutions, from data preprocessing to model deployment and monitoring
• Develop and optimize agentic Large Language Models (LLMs) solutions for healthcare applications using techniques like fine-tuning and Retrieval-Augmented Generation (RAG)
• Create robust data pipelines for validation, and deployment
• Implement ML systems that can effectively process and analyze diverse healthcare data types, including structured clinical data, medical imaging, and unstructured text
• Collaborate with backend engineers to integrate ML models into our production infrastructure
• Ensure ML systems meet strict healthcare compliance requirements while maintaining high performance
• Design and implement monitoring systems for model performance, data drift, and system health
• Lead research initiatives to explore and implement state-of-the-art AI/ML techniques
• Collaborate with clinical experts to validate model outputs and ensure medical accuracy
• Stay current with the latest developments in AI/ML, particularly in healthcare applications