Transform healthcare delivery by applying advanced machine learning expertise to impact patient care and operational efficiency.
Requirements
- 5+ years of software engineering experience with a focus on production-grade ML systems, backend infrastructure, or MLOps.
- Strong proficiency in Python and SQL, with experience in efficient, maintainable ML code development.
- Hands-on experience with modern LLM models, transformers, and deploying ML models in production environments.
- Experience with ML pipeline development, model versioning, experimentation, and reproducibility best practices.
- Familiarity with cloud ML tools (e.g., AWS SageMaker, Bedrock) and data infrastructure.
Responsibilities
- Lead AI and ML initiatives, designing and implementing production-grade machine learning systems and pipelines.
- Develop scalable infrastructure for model training, evaluation, and deployment, ensuring reliability and observability.
- Assemble, analyze, and interpret large, complex datasets to meet functional and strategic requirements.
- Establish and promote data engineering best practices and standards for efficient access and use across teams.
- Implement evaluation frameworks to rigorously assess model performance, accuracy, and fairness.
- Continuously improve ML infrastructure for scalability, stability, and security.
- Create internal tools and libraries to enhance workflow efficiency for analytics and data science teams.
Other
- Collaborate with cross-functional teams including Product, Clinical, Data, and Design to drive innovation and efficiency.
- Excellent analytical, problem-solving, and communication skills, with the ability to work in a remote, cross-functional team.
- Healthcare experience is valuable but not required; entrepreneurial mindset and ability to explain complex technical concepts.
- Competitive base salary ($175,000–$200,000) plus equity and additional benefits.
- Comprehensive healthcare, dental, and vision coverage.
