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For Companies At a Glance
- Tasks: Join cutting-edge AI teams to develop solutions in life sciences and advanced industries.
- Company: Be part of McKinsey, a leader in scientific AI and technology development.
- Benefits: Enjoy opportunities for mentorship, collaboration, and working with innovative technologies.
- Why this job: Make an impact by solving complex problems and shaping the future of AI in various industries.
- Qualifications: Master's or PhD in Computer Science/Engineering with relevant experience in machine learning and cloud architecture.
- Other info: Work in multi-disciplinary teams and mentor junior colleagues while enhancing McKinsey’s AI Toolbox.
The predicted salary is between 48000 - 84000 ÂŁ per year.
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
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Senior Machine Learning Engineer - Scientific AI employer: McKinsey & Company, Inc.
Contact Detail:
McKinsey & Company, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer - Scientific AI
✨Tip Number 1
Fokussiere dich auf deine Erfahrungen mit Cloud-Architekturen und der Bereitstellung von End-to-End-Entwicklungsumgebungen. Stelle sicher, dass du konkrete Beispiele parat hast, wie du diese Technologien in frĂĽheren Projekten eingesetzt hast.
✨Tip Number 2
Betone deine Kenntnisse in der GPU-Modellentwicklung und -bereitstellung. Zeige, wie du komplexe Modelle optimiert und in produktive Umgebungen integriert hast, um potenzielle Arbeitgeber zu beeindrucken.
✨Tip Number 3
Bereite dich darauf vor, technische Konzepte für nicht-technische Stakeholder verständlich zu erklären. Dies wird dir helfen, deine Kommunikationsfähigkeiten zu demonstrieren, die für die Rolle entscheidend sind.
✨Tip Number 4
Zeige deine Fähigkeit zur Zusammenarbeit in multidisziplinären Teams. Teile Beispiele, wie du erfolgreich mit anderen Fachbereichen zusammengearbeitet hast, um AI/ML-Lösungen zu integrieren und Probleme zu lösen.
We think you need these skills to ace Senior Machine Learning Engineer - Scientific AI
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description for the Senior Machine Learning Engineer position. Understand the key responsibilities and required skills, especially in areas like AI, data engineering, and cloud architecture.
Highlight Relevant Experience: In your CV and cover letter, emphasize your experience in machine learning, GPU model development, and cloud deployment. Use specific examples that demonstrate your expertise in these areas, as they are crucial for this role.
Showcase Your Projects: Include details about any relevant projects you've worked on, particularly those involving AI/ML solutions. Describe your role, the technologies used, and the impact of your work to illustrate your capabilities.
Tailor Your Application: Customize your application materials to reflect the language and priorities outlined in the job description. Use keywords related to scientific AI, data transformation, and engineering roadmaps to align with McKinsey’s expectations.
How to prepare for a job interview at McKinsey & Company, Inc.
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with machine learning, cloud architecture, and data transformation in detail. Highlight specific projects where you've successfully implemented AI solutions, especially in life sciences or advanced industries.
✨Demonstrate Problem-Solving Skills
Expect to face complex client problems during the interview. Prepare examples of how you've approached similar challenges in the past, focusing on your role in developing engineering roadmaps and transforming prototypes into deployment-ready solutions.
✨Communicate Effectively with Stakeholders
Since you'll be translating engineering concepts for senior stakeholders, practice explaining technical topics in a clear and concise manner. Use analogies or simplified terms to ensure understanding, especially for non-technical audiences.
✨Emphasize Collaboration and Mentorship
Highlight your experience working in multi-disciplinary teams and mentoring junior colleagues. Share specific instances where your collaboration led to successful project outcomes, showcasing your ability to integrate AI/ML solutions across various projects.
