Machine Learning Systems Research Intern at Red Hat working on AI inference and model optimization techniques. Collaborating with experts in the field while gaining hands - on experience in applied ML research.
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
Requirements
- 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
Benefits
- Health insurance
- Life insurance
- Disability insurance
- Financial benefits
- Retirement benefits
- Paid leave
- Professional development
- Tuition assistance
- Work-life programs
- Dependent care
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