• 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

• Be the technical owner of a new, complex machine learning project • Work closely with Evolution’s ML product manager • Start with mapping out the opportunity and continue to launching a solution in production • Acted as owner of applied ML projects from conception to shipping • Part of an ML team that shipped products

• Develop and evaluate scalable deep learning algorithms that are central to our brain decoding initiatives. • Collaborate closely with data scientists to pioneer research in generative modeling and representation learning. • Identify bottlenecks in data processing pipelines and devise effective solutions, improving performance and reliability. • Maintain high standards of code quality, organization, and automatization across all projects. • Adapt machine learning and neural network algorithms to optimize performance in various computing environments, including distributed clusters and GPUs. • Write and revise papers, communicate and disseminate results.