About the Role
Role details below.
Responsibilities
- Drive AI initiatives and collaborate with teams to leverage data effectively through model development and evaluation
- Design and implement scalable Machine Learning infrastructure and solutions, ensuring reliability and performance
- Help establish data engineering best practices and promote standards that enhance data accessibility across teams
- Create and maintain optimal AI pipeline architecture with high observability and robust operational characteristics
- Champion responsible AI development by implementing and reviewing models that maximize data value while ensuring fairness and equity
- Assemble large, complex data sets that address functional and strategic requirements
- Partner with other teams such as: Executive, Product, Clinical, Data, and Design
- Identify, design, and implement process improvements to enhance efficiency and scalability
- Create tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader
- Develop efficient, reliable AI pipelines with strong monitoring and observability
- Navigate and optimize our data ecosystem to drive meaningful insights
- Design and implement comprehensive evaluation frameworks and benchmarks to rigorously assess model performance, accuracy and reliability
Requirements
- 5+ years of software engineering experience, with a focus on building production-grade machine learning systems, backend infrastructure, or MLOps
- Graduate degree in Computer Science, Statistics, or related quantitative field
- Strong proficiency in Python and SQL, with the ability to create efficient and maintainable code for machine learning applications
- Developing and maintaining ML pipelines for model training, evaluation, deployment
- Hands-on experience in developing and implementing modern LLM models and transformers and deploying ML models in a production environment
- Hands-on experience in designing and implementing best practices for model versioning, experimentation, and reproducibility
- Hands-on experience in continuously improving our ML infrastructure for stability, scalability, observability, and security
- Hands-on experience in developing internal tooling and libraries to enhance ML workflow efficiency
Nice to Have
- Experience with tools like AWS Sagemaker or Bedrock
- Healthcare experience is valuable but not required
