• Working with our distributed training stack to start training runs and tracking them closely. • Contributing to our ML codebases and adding new models, algorithms and features. • Experimenting with new model architectures and designs, and getting them production ready. • Training and finetuning models, and adapting them to our products by taking tester feedback and iterating over the trained models.

• Design scalable data pipelines and infrastructure for enterprise ML systems • Implement ML models and systems into production • Collaborate with data scientists and software engineers • Deploy scalable tools and services for machine learning training and inference • Evaluate new technologies to improve ML system performance and reliability • Apply software engineering best practices, including CI/CD, to ML development • Facilitate the development and deployment of ML proof-of-concepts • Review, refactor, optimize, containerize, deploy, version, and monitor ML models • Implement monitoring and alerting solutions to ensure the reliability and performance of machine learning systems • Optimize and automate the machine learning deployment process to ensure efficiency and reproducibility • Collaborate with cross-functional teams to troubleshoot and resolve issues related to machine learning deployments • Stay updated with industry trends and apply knowledge to drive innovation • Promote industry best practices and enhance team expertise

• Develop solutions for real world, large scale problems. • Design, develop, test, maintain and improve Machine Learning models. • Manage project priorities, deadlines, and deliverables.

• 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