About the Company:
Caire is building a next-generation, virtual-first care platform that puts the patient at the center. This platform will deliver best-in-class integrated shared services and technology to support the launch of multiple new virtual care companies over time. Caire was born out of a novel joint venture between Aegis Ventures, a venture capital studio creating transformative healthcare companies, and Northwell Health, New York’s largest health system. Through this unique arrangement, Caire operates as an independent health tech start up but enjoys the benefits of existing within this larger healthcare ecosystem.
Caire is building the scalable infrastructure which will serve as the backbone for our initial virtual care use cases. Through the creation of a shared operating system across every core function, this infrastructure will minimize time on operations while maximizing time on what really matters – high quality patient care that delivers improved outcomes.
Caire is supported by a multidisciplinary team of clinical advisors, product experts, experienced engineers, and business strategists who are passionate about improving healthcare for all. Come reimagine the future of healthcare with us!
Role Description:
Caire is seeking a passionate and experienced ML Engineer to help launch Caire’s AI-native virtual care platform. As a member of the team, you will be responsible for designing and implementing machine learning models, conducting data analysis, and improving existing ML algorithms. Your expertise in building state-of-the-art, robust machine learning models and inference pipelines will be crucial to our success.
What you will do:
- Researching and implementing appropriate ML algorithms and tools across predictive analytics and natural language processing (including large language models)
- Supporting the engineering and data teams in selecting appropriate data representations
- Conducting machine learning experiments
- Performing statistical analysis of inference results, tuning, fine-tuning, and prompt engineering for achieving optimal model performance
- Architect, build, and maintain MLOps pipelines to streamline model development, training, deployment, and monitoring.
- Optimize and fine-tune existing models to improve accuracy, efficiency, and scalability.
- Stay up-to-date with the latest advancements in ML technologies and industry best
practices
What you bring:
- At least five years of experience as a Machine Learning Engineer or similar role
- AI & ML experience: natural language processing, large language models, prompt engineering, fine-tuning, retrieval augmented generation using vector databases, transformers, AI explainability and interpretability; familiarity with relevant libraries and frameworks: NLTK, TensorFlow, PyTorch, scikit-learn, LangChain
- Cloud Computing, Data Lakehouse & MLOps experience: AWS (preferred), GCP, Databricks, Spark, MLFlow
- Hands-on experience in building MLOps pipelines, deploying models in production, and maintaining them at scale.
- Foundational experience: statistical modeling and inference, time series analysis and predictive modeling; supervised learning; data mining, model evaluation, feature selection, and dimensionality reduction
- Experience working with Python and web frameworks such as FastAPI or Flask
- Experience working in an agile environment (standups, sprint planning, retrospectives, etc.)
- Ability to collaborate and communicate effectively within a team environment
- Experience working with sensitive data such as PII or PHI is a plus
- Experience at a fast-paced, high-growth company is a plus
Perks:
- We’re welcome new ideas and allows you to make an immediate impact on the team
- 4 weeks PTO + 1 week of sick time
- Personal laptop
- Health and wellness package
- Remote work
