Technical Lead Manager, Applied Ai, Deepmind

Google Google · Big Tech · Singapore

Technical Lead Manager at DeepMind, Google, focused on leading teams to develop and deploy novel applications leveraging generative AI models. The role involves translating AI research into real-world products, shipping software, scaling products from concept to production, and optimizing ML infrastructure. Requires strong software engineering, leadership, and experience in fast-paced, early-stage environments.

What you'd actually do

  1. Lead the architecture, design, and development of new scalable products and software applications from 0 to 1, leveraging generative AI models.
  2. Guide the team in rapidly developing new features, iterating quickly based on evaluation results.
  3. Collaborate with researchers and product managers to translate research advancements into tangible product features, and partner with the PM team to qualify and seize new project opportunities.
  4. Mentor and develop team members, fostering a collaborative and high-performing environment.
  5. Oversee the optimization of software performance, ensure the reliability of deployed applications, and advocate best practices for building and deploying generative AI applications.

Skills

Required

  • software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript)
  • leading technical projects
  • optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning)
  • people leadership role overseeing and guiding technical teams
  • experience in one or more ML fields (e.g., media generation, reinforcement learning, infrastructure, etc.)

Nice to have

  • generative AI models and applications
  • evaluating model performance
  • analyzing results
  • implementing improvements
  • front-end development
  • rapidly developing and shipping software products in a fast-paced, customer-facing startup-like environment
  • adaptability to changing priorities
  • cloud computing platforms and infrastructure (e.g., Google Cloud Platform)
  • machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, etc.
  • Contributions to open-source projects

What the JD emphasized

  • lead the development and deployment of novel applications
  • rapidly developing new features
  • delivering solutions
  • maximize impact
  • translating AI research into real-world products
  • demonstrating the capabilities of latest generation models
  • strong track record of building and shipping software
  • leading engineering teams
  • early-stage environments
  • scaling products from initial concept to production
  • drive product and business impact
  • develop strong relationships with research teams
  • strong software engineering foundation
  • passion for building and iterating on software products
  • proven leadership skills
  • grow in fast-moving environments
  • proven ability to deliver high-quality code
  • guide a team to success
  • experience in end-to-end development
  • ability to mentor and develop junior engineers
  • experience in early-stage or startup environments
  • managers take ownership and drive product development from the ground up
  • pioneering AI lab
  • advancing AI development
  • solve complex global challenges
  • accelerate high-quality product innovation
  • widespread public benefit
  • scientific discovery
  • safety and ethics are always our highest priority
  • pushing the boundaries
  • learning opportunities
  • varied career pathways
  • achieve exceptional results
  • collective effort
  • architecture, design, and development of new scalable products and software applications from 0 to 1
  • leveraging generative AI models
  • rapidly developing new features
  • iterating quickly based on evaluation results
  • translate research advancements into tangible product features
  • partner with the PM team to qualify and seize new project opportunities
  • Mentor and develop team members
  • fostering a collaborative and high-performing environment
  • Oversee the optimization of software performance
  • ensure the reliability of deployed applications
  • advocate best practices for building and deploying generative AI applications
  • optimizing ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • evaluating model performance
  • analyzing results
  • implementing improvements

Other signals

  • leading development and deployment of novel applications
  • translating AI research into real-world products
  • shipping software
  • scaling products from initial concept to production
  • building and shipping software products
  • delivering high-quality code
  • driving product development from the ground up
  • architecting, designing, and developing new scalable products
  • leveraging generative AI models
  • rapidly developing new features
  • iterating quickly based on evaluation results
  • translating research advancements into tangible product features
  • optimizing software performance
  • ensuring reliability of deployed applications
  • advocating best practices for building and deploying generative AI applications
  • optimizing ML infrastructure
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning
  • evaluating model performance
  • analyzing results
  • implementing improvements