Help transform healthcare through AI
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
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
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

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 (experience with tools like AWS Sagemaker or Bedrock is preferred)
Healthcare experience is valuable but not required
Developing and implementing modern LLM models and transformers and deploying ML models in a production environment
Designing and implementing best practices for model versioning, experimentation, and reproducibility
Continuously improving our ML infrastructure for stability, scalability, observability, and security
Developing internal tooling and libraries to enhance ML workflow efficiency.

Senior Machine Learning Engineer

Key skills