Job Type
Job Details
- PhD in Computer Science, a related technical field, or equivalent practical experience.
- 5 years of experience in research.
- Experience with general purpose programming languages (e.g., C/C++ or Python).
- Experience in contributing to research communities, including publishing in forums (e.g., ACL, EMNLP, NeurIPS, ICML).
Preferred qualifications:
- Experience working in a fluid environment, bridging engineering and research by mixing rapid prototyping and experimentation.
- Knowledge of Tensorflow and Jax.
- Ability to communicate with client and platform teams.
- Passion for machine learning and natural language processing.
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.
The researchers in Bengaluru are part of Google’s global network of researchers participating in conferences, publishing research in scientific papers, and collaborating with one another.
A conversational AI tool that enables users to collaborate with generative AI and help augment their imagination, expand their curiosity, and enhance their productivity.
- Generate new hypotheses (e.g., some recipes), perform experimentation, analyze pros/cons, and redesign. Evaluate and analyze the proposals at both the quantitative and qualitative levels, to identify their limitations and avenues for improvement.
- Take initiative and be willing to complete a variety of tasks.
- Exhibit a growth-focused approach and pursue continuous learning of new skills.
- Stay informed about the latest research and developments in the field.
- Manage data generation, logging, and evaluation. Measure resource (TPU, CPU, memory) consumption, and making trade-offs to adapt to hardware of diverse capabilities.
