Minimum qualifications:

• PhD in Computer Science, a related technical field, or equivalent practical experience.
  
• 7 years of experience in Machine Learning (ML), ML Efficiency, ML Optimization, or a related field.
  
• Experience contributing to research communities including publishing in forums (e.g., ICML, ICLR, NeurIPS, or related).
  
• Experience with programming languages (e.g., Python or C/C++).
  

Preferred qualifications:

• Experience in innovative research.
  
• Experience working with a research team.
• Ability to effectively navigate ambiguity.
• Excellent coding skills.
  

About the job

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

Our team is committed to advancing in the areas of efficient architectures, training efficiency of foundational models, data efficiency, and inference efficiency.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.

Responsibilities

• Develop fundamental advances in algorithms and foundational model architectures that improve the speed of training and generalization of deep learning models.
• Develop fundamental advances to make inference with foundational models more efficient and flexible including knowledge adoption and distillation techniques.
• Work on data subset selection and more efficient ways to train with large data sets.
• Improve entire model deployment pipeline including better formulations for pretraining, instruction and tuning, and Reinforcement Learning from Human Feedback (RLHF).