Manager, Applied AI Engineering, Deepmind

Google Google · Big Tech · London, United Kingdom

Manager for an Applied AI Engineering team at Google DeepMind, focused on leading the development and deployment of generative AI applications, translating research into products, and shipping software features for billions of users.

What you'd actually do

  1. Lead a team in the design, development, and deployment of scalable generative AI applications and advocating industry best practices.
  2. Lead the team through the rapid development of new features, iterating based on evaluation results while mentoring members to cultivate a collaborative and high-performing environment.
  3. Collaborate with researchers and product managers to translate research advancements into tangible product features.
  4. Oversee the optimization of software performance and ensure the reliability of deployed applications.
  5. Lead the architecture and development of new products and features from 0 to 1.

Skills

Required

  • software development experience
  • system design
  • data structures
  • algorithms
  • technical project strategy
  • ML design
  • ML infrastructure optimization
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • technical leadership
  • people management
  • team leadership

Nice to have

  • media generation
  • reinforcement learning
  • sequential decision making
  • ML infrastructure
  • generative AI research
  • model performance evaluation
  • TensorFlow
  • PyTorch
  • Hugging Face
  • rapid software product development
  • open-source contributions

What the JD emphasized

  • leading technical project strategy
  • ML design
  • optimizing industry-scale ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • technical leadership role
  • people management
  • supervision/team leadership role
  • generative AI applications
  • rapid development of new features
  • optimize software performance
  • reliability of deployed applications
  • architecture and development of new products and features from 0 to 1

Other signals

  • leading engineering teams
  • translating AI research into real-world products
  • building and shipping software
  • rapidly developing new features
  • optimizing software performance
  • demonstrating the capabilities of latest generation models