Senior Machine Learning Engineer - Scientific AI

Your Growth

You will work with cutting-edge AI teams on research and development topics across our life sciences, global energy and materials, and advanced industries practices, serving as a data engineer/machine learning engineer in a technology development and delivery capacity. With your expertise in computer science, computer engineering, cloud, and data transformation (ETL & feature engineering), you will help build and shape McKinsey’s scientific AI offering. As a member of McKinsey’s global scientific AI team, you will address industry questions on how AI can be used for therapeutics, chemicals, and materials (including small molecules, proteins, mRNA, polymers, etc.). Your work will involve delivering distinctive capabilities, data, and machine learning systems through collaboration with client teams, playing a pivotal role in creating and disseminating cutting-edge knowledge and proprietary assets, and building the firm’s reputation in your area of expertise.

Your Impact

You will leverage your expertise in data/machine learning engineering and product development to solve complex client problems through part-time staffing, develop engineering roadmaps for cell-level initiatives, and transform AI prototypes into deployment-ready solutions. By working directly with client delivery teams, you will ensure seamless implementation of prototypes and solutions. You will translate engineering concepts for senior stakeholders, write optimized code to enhance McKinsey’s AI Toolbox, and codify methodologies for future deployment. In multi-disciplinary teams, you will ensure smooth integration of AI/ML solutions across projects and mentor junior colleagues.

Your qualifications and skills

• Degree in Computer Science, Computer Engineering, or equivalent experience
• Master’s degree with 5-7 years of relevant experience or PhD with 2-5 years of relevant experience
• Experience in research Machine Learning Experience (MLE Path)
• GPU Model Development & Deployment
• Deep Learning Model Maintenance
• Model Retraining Cycles
• Cloud Architecture
• Deployment of End-to-End (E2E) Development Environments
• Deep Kubernetes (K8s) Knowledge
• Node Pools
• Carpenter
• Ray Security (Authentication & Authorization)
• Kubernetes Networking (Load Balancing, Proxy, DNS)
• Terraform