The role
From Google's own posting.
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Make core contributions to Gemini that advance what digital agents can do and are deployable into production.
Identify model areas of improvement.
Train and evaluate agents across various digital environments.
Focus on multi-step reinforcement learning in multimodal, digital environments.
Minimum qualifications:
Bachelor’s degree in Computer Science, a similar field, or equivalent practical experience.
5 years of experience designing, training, and evaluating machine learning models.
Experience in Python programming.
Experience in test writing for ML purposes either in Tensor flow or similar for post-silicon data collection, and pre-silicon modeling and correlation.
Experience with Large Language Models, NLP, or Generative AI.
Preferred qualifications:
Experience in building, scaling, and training/fine-tuning AI models and systems.
Deep expertise in Reinforcement Learning.
Bachelor’s degree in Computer Science, a similar field, or equivalent practical experience.
5 years of experience designing, training, and evaluating machine learning models.
Experience in Python programming.
Experience in test writing for ML purposes either in Tensor flow or similar for post-silicon data collection, and pre-silicon modeling and correlation.
Experience with Large Language Models, NLP, or Generative AI.