Lead, Generative AI Engineering & ML Infra

Google Inc.; Greater London; On-site; GBP 150,000 - 230,000; Full time

Job summary

Google DeepMind London is seeking an experienced ML engineering leader to drive the development and deployment of novel generative AI applications. You will translate cutting-edge research into real-world features and lead engineering teams across partner groups to deliver impactful solutions at scale.

You will push for rapid feature development, iterating on evaluation results, mentoring staff, and ensuring robust performance of deployed systems in production environments.

Qualifications

• Bachelor's degree in Electrical Engineering, Computer Engineering, CS, or related field or equivalent practical experience.
• 8 years software development experience including system design, data structures and algorithms.
• 7 years leading technical project strategy, ML design, and ML infra optimization.
• 5 years in a technical leadership and people management role.

Responsibilities

• Lead design, development and deployment of scalable generative AI applications.
• Drive rapid feature development, iterate on results, mentor team members.
• Collaborate with researchers and product managers to translate research into product features.
• Oversee software performance and reliability of deployed apps.
• Lead architecture and development of new products from 0 to 1.

Skills

Software development

System design

Algorithms

Leadership

Education

Bachelor's degree in Electrical/Computer Engineering or CS

Tools

TensorFlow

PyTorch

Hugging Face

Job description

Google DeepMind London is seeking an experienced ML engineering leader to drive the development and deployment of novel generative AI applications. You will translate cutting-edge research into real-world features and lead engineering teams across partner groups to deliver impactful solutions at scale.

You will push for rapid feature development, iterating on evaluation results, mentoring staff, and ensuring robust performance of deployed systems in production environments.