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Senior Machine Learning Engineer - Scientific AI

McKinsey & Company
London
5 months ago
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Driving lasting impact and building long-term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture - doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward.
In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleagues—at all levels—will invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won’t find anywhere else.
When you join us, you will have:
  • Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey.
  • A voice that matters: From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
  • Global community: With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
  • World-class benefits: On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well-being for you and your family.
Your role will be split between developing ML pipelines, scaling AI models, leading architectural discussions, shaping engineering roadmaps and deploying solutions directly with the client delivery teams. 
You will leverage your expertise in architecting, orchestrating and scaling complex AI/ML pipelines along with your product development mindset to solve complex problems and create solutions that will accelerate our clients in their respective fields.
We expect you to drive engineering roadmaps for cell-level initiatives and transform AI prototypes into deployment-ready solutions. You will translate engineering concepts for senior stakeholders, enhance McKinsey’s AI Toolbox, and codify methodologies for future deployment.
By working directly with client delivery teams, you will ensure seamless implementation and operationalization of cutting-edge solutions and prototypes. In multi-disciplinary teams, you will ensure smooth integration of AI/ML solutions across projects and mentor junior colleagues.
As a Senior Machine Learning Engineer, you’ll be asked to optimize and scale complex AI applications multi machine and multi-GPU setups. You will ensure the latest tools and technologies are used and support your colleagues with codifying and distributing knowledge.
You will be working in our London office in our Life Sciences practice. 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 machine learning engineer in a technology development and delivery capacity.
You will be on McKinsey’s global scientific AI team helping to answer industry questions related to how AI can be used for therapeutics, chemicals & materials (including small molecules, proteins, mRNA, polymers, etc.). With your expertise in computer science, computer engineering, cloud, and distributed computing you will help build and shape McKinsey’s Scientific AI offering.
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.
  • Degree in computer science, computer engineering, or equivalent experience
  • Master’s degree with 5+ years of relevant experience or PhD with 2+ years of relevant experience
  • Machine Learning Experience (architecting, deploying, orchestrating, scaling ML/AI solutions)
  • Extensive Kubernetes experience (3+ years)
  • Experience in distributed/parallel processing/computing ideally with hands-on experience on Karpenter or Ray
  • Multi-stage deployment orchestration (dev/QA/prod)
  • CI/CD pipelines
  • Cloud Architecture (at least one of AWS, Azure, GCP)
  • GPU Model Deployment
  • ML Model lifecycle management (MLFlow, etc.)
  • Security Architecture on Cloud (Authentication & Authorization)
  • Kubernetes Networking (Load Balancing, Proxy, DNS) Terraform

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