Intern developing machine learning tools within ADEO Services' AI Factory team. Collaborating on software quality and automation alongside a senior ML engineer.
About the role
- Scientific AI & ML Engineer designing and deploying innovative AI-driven solutions. Collaborating with teams to solve complex scientific challenges through advanced machine learning techniques.
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 machine learning and AI 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, 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
- 4+ years of experience with Object-Oriented Programming (OOP)
- 3+ years of experience developing AI and ML models and solutions using distributed and cloud technologies, including Azure or Databricks
- 3+ years of experience developing, validating, and deploying scientific AI and ML workflows, including data preparation, model training, and model monitoring
- Experience building containerized applications, including API design and secure authentication
- Knowledge of AI and ML concepts, including supervised and unsupervised learning, statistical modeling, and deep learning methods
- Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements
- Bachelor’s degree in a Computer Science or Data Science field
Benefits
- health, life, disability, financial, and retirement benefits
- paid leave
- professional development
- tuition assistance
- work-life programs
- dependent care
- recognition awards program for exceptional performance
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