Staff Research Engineer, Applied Ai, Deepmind

Google Google · Big Tech · Singapore

Research Engineer to lead development and deployment of novel applications using Google's generative AI models. Focus on rapidly developing new features, collaborating with partner teams, and delivering solutions to maximize impact for Google and its customers. Role involves translating AI research into real-world products, demonstrating model capabilities, and scaling products from concept to production in fast-paced, early-stage environments.

What you'd actually do

  1. Partner closely with external parties and internal cross-functional teams to navigate ambiguity, deeply understand real-world challenges, and define clear product objectives and technical designs.
  2. Drive the curation of specialized datasets, design rigorous evaluations across various industry verticals, and execute model fine-tuning to achieve optimal real-world performance.
  3. Lead the engineering and development of novel solutions from 0 to 1, utilizing internal platforms and tools to build sophisticated agents and workflows powered by GDM foundation models.
  4. Synthesize and upstream learnings from third-party partners to the core research teams by sharing real-world evaluations, edge cases, and deployment signals which can inform the development of future frontier models.
  5. Act as a technical lead in the applied AI space, setting best practices for genAI deployment and demonstrating the peak capabilities of frontier models in solving high-impact problems end-to-end.

Skills

Required

  • Python
  • data structures
  • algorithms
  • ML design
  • ML infrastructure optimization
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine-tuning
  • media generation
  • reinforcement learning
  • software testing
  • software launching
  • software design
  • software architecture

Nice to have

  • generative AI models and applications
  • 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
  • translating cutting-edge AI research into real-world products
  • demonstrating the capabilities of latest generation models
  • strong track record of building and shipping software
  • scaling products from initial concept to production
  • drive product and business impact
  • strong software engineering foundation
  • passion for building and iterating on software products
  • using state-of-the-art GenAI tools
  • thrive in fast-paced environments
  • proven ability to deliver high-quality code
  • prototyping, building, and scaling software is essential
  • experience in early-stage or startup environments
  • take ownership and drive product development from the ground up
  • advancing AI development to solve complex global challenges
  • accelerate high-quality product innovation for billions of users
  • pushing the boundaries across multiple domains
  • achieve exceptional results through collective effort
  • navigate ambiguity
  • deeply understand real-world challenges
  • define clear product objectives and technical designs
  • curation of specialized datasets
  • design rigorous evaluations
  • execute model fine-tuning
  • achieve optimal real-world performance
  • engineering and development of novel solutions from 0 to 1
  • build sophisticated agents and workflows
  • synthesize and upstream learnings
  • core research teams
  • real-world evaluations
  • edge cases
  • deployment signals
  • inform the development of future frontier models
  • technical lead in the applied AI space
  • setting best practices for genAI deployment
  • demonstrating the peak capabilities of frontier models
  • solving high-impact problems end-to-end
  • 8 years of experience in software development
  • 5 years of experience in ML design and optimizing ML infrastructure
  • model deployment
  • evaluation
  • data processing
  • debugging
  • fine-tuning
  • media generation
  • reinforcement learning
  • 5 years of experience testing, and launching software products
  • 3 years of experience with software design and architecture
  • generative AI models and applications
  • rapidly developing and shipping software products
  • fast-paced, customer-facing startup-like environment
  • adaptability to changing priorities

Other signals

  • Translating cutting-edge AI research into real-world products
  • Demonstrating the capabilities of latest generation models
  • Building and shipping software
  • Scaling products from initial concept to production
  • Drive product and business impact
  • Leveraging Google’s generative AI models
  • Rapidly developing new features
  • Working across partner teams to deliver solutions
  • Maximize impact for Google and top Google customers