Research Scientist - Generative AI & Multimodal Systems

Google LLC

Mountain View (CA)

In loco

USD 207.000 - 300.000

Tempo pieno

12 ore fa
Candidati tra i primi

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Descrizione del lavoro

Google DeepMind is seeking a Research Scientist to set up large-scale tests and deploy promising ideas quickly, while coordinating deadlines and applying the latest theories to develop new products, processes, or technologies.

You will publish findings and contribute to a broad range of AI research areas, collaborating with global partners to advance Gemini models and ML efficiency across multiple domains.

Competenze

  • Bachelor’s degree or equivalent practical experience.
  • 2 years of experience building and shipping technical products.
  • 2 years of experience leading a research agenda.
  • Experience in generative AI and Large Language Models (LLM).
  • Master’s degree or PhD in Engineering, Computer Science, or related field (preferred).
  • 3 years of research experience (e.g., model training/deployment, data pipeline design, ML infra).
  • 2 years of experience leading research efforts and influencing others.
  • Publications in conferences, journals, or public repositories.

Mansioni

  • Set up large-scale tests and deploy ideas quickly and broadly.
  • Publish findings and share research with the wider community.
  • Drive research innovations from conception to production.
  • Define data structures, frameworks, and evaluation metrics for research solutions.

Conoscenze

LLM experience
Research leadership
Publications

Formazione

Bachelor’s degree or equivalent practical experience
Master’s degree or PhD in Engineering or CS

Descrizione del lavoro

Google DeepMind is seeking a Research Scientist to set up large-scale tests and deploy promising ideas quickly, while coordinating deadlines and applying the latest theories to develop new products, processes, or technologies.

You will publish findings and contribute to a broad range of AI research areas, collaborating with global partners to advance Gemini models and ML efficiency across multiple domains.

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