Applied Research Scientist, Natural Language Processing, Google Research
View: 154
Update day: 20-03-2024
Category: Worker IT - Hardware / Networking Information Technology Legal / Contracts
Industry: Informatique
Job content
Minimum qualifications:
- PhD degree in Computer Science or related technical field or equivalent practical experience.
- Experience working with Python, C++, or TensorFlow.
- Experience in natural language processing (NLP), recommendation agents, or language generation.
Preferred qualifications:
- Experience with contributing to a launched ML-powered feature substantially or publishing at ML/NLP conferences.
- Deep knowledge of neural networks, natural language processing, and the fundamental algorithms in the field.
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.
The mission of our team is to make mobile devices smarter using textual and screen understanding, where much of the processing happens on the device itself. We stand on the intersection of research and applications. Some projects that we work on are Grammar Error Correction, Smart Text Selection, and Federated Learning.
As an Applied Research Scientist, you will develop cutting-edge text and screen understanding technologies. You will come with outstanding empirical research and software engineering skills, with familiarity with with natural language processing (NLP) and modern ML models for language processing (e.g., Transformers, T5).
Responsibilities
- Conduct end-to-end applied natural language research and development (mostly on-device focused).
- Create new datasets and train ML models on these datasets. Iterate on the quality of the models to achieve exceptional results.
- Develop infrastructure for evaluation, productization, and monitoring of the model deployments.
- Build prototypes of new ideas in rapid sprints. Suggest approaches and methods from recent advances in the ML/NLP field, and drive their adoption within the team.
Deadline: 04-05-2024
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