About the role

Scientific AI and ML Engineer designing and deploying AI solutions to solve complex scientific challenges. Collaborating with a multidisciplinary team focused on health and technology applications.

Responsibilities

Develop and optimize novel AI and ML algorithms tailored to scientific challenges, integrating domain knowledge to ensure results are actionable and relevant
Design, validate, and deploy end-to-end AI and ML workflows in cloud environments to address complex analytical needs across the organization
Collaborate with cross-functional teams to design efficient frameworks for data preparation, feature engineering, model selection, and outcome interpretation across data sources
Build tools and infrastructure to enable seamless experimentation, rapid model iteration, and reproducibility of scientific AI and ML experiments
Scale AI and ML solutions using advanced techniques such as distributed computing, cloud environments, including Azure and Databricks, and containerized deployments
Implement automated pipelines for training, validating, and deploying models into production with rigorous monitoring and evaluation processes
Develop containerized applications and APIs for exposing AI and ML model capabilities, ensuring accessibility and interpretability for stakeholders
Identify and introduce state-of-the-art AI and ML techniques and tools such as explainable AI (XAI), reinforcement learning, and probabilistic modeling, to enhance research outcomes and operational decision-making
Support collaboration with data scientists, researchers, and engineers to bridge the gap between foundational AI and ML research and deployed, impactful applications

5+ years of experience across data science, AI, and data engineering with ownership of end-to-end analytical or ML solutions
5+ years of experience in bioinformatics or computational biology, including analysis and processing of biological and imaging data such as FASTQ, BAM / CRAM, VCF, or DICOM
3+ years of experience designing and deploying AI and ML solutions, including model training, evaluation, and production
3+ years of experience designing cloud architectures for data-intensive or AI applications on AWS, Azure, or Google Cloud
Experience with cloud-based AI platforms, including Databricks, AWS SageMaker, or Azure ML
Knowledge of Python or R for data analysis, modeling, and pipeline development
Ability to translate complex biological questions into analytical approaches and apply existing methods to novel datasets
Ability to work independently, lead technical initiatives, and deliver in a fast-paced, evolving environment
Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements
Bachelor’s degree in a Science field