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 RoboRater team works on utilization LLMs for real-world tasks, both in the space of rating real-world scenarios, performing personalized evaluation, and for simulating users to accurately model real-world preferences, behaviors, and usage.
Our team has many researchers with world-class publications, we build research capabilities that are delivered in products like Search and Ads.
Responsibilities
- Understand and stay current on academic literature on Large Language Model's (LLM) capabilities, and research on autorating (through LLMs, and otherwise), user simulation, and evaluation.
- Understand the current system, and the needs for rating and agentic tasks in the Google context.
- Being able to build and deliver functioning systems to Google products, with the goal to improve the next generation of products, for the user, while keeping in mind Google's serving constraints and systems.
Qualifications
Minimum qualifications:
- PhD in Computer Science, a related field, or equivalent practical experience.
- One of more scientific publication submissions for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
Preferred qualifications:
- 2 years of coding experience.
- Ability to work well in a team and willingness to accept new ideas.
- Proven track record of delivering software engineering systems, with an understanding of existing ML literature.