Opis stanowiska

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.
In this role, you will get to work on Kaggle’s Research priorities, help innovate in Kaggle’s leading platform for ML competitions, datasets, and models, and establish Kaggle Research as the leader in building earned trust through community validation of AI.
The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.

- Work with the Kaggle Competitions team to expand Kaggle's competitions platform to host and run LLM Benchmarks and community evaluations.
- Collaborate with Google DeepMind Researchers and external researchers to create and host valuable AI evaluations and benchmarks on Kaggle.
- Contribute to the development and deployment of new research methods and algorithms for AI and ML. Mentor and guide junior engineers on the team.
- Publish research papers and present findings at conferences and workshops. Collaborate with engineering, design, and product leadership to improve Kaggle's product for Research priorities.
- Collaborate with other members of the Kaggle Research team to create the next generation of benchmarking and evaluation methods and structures for AI / ML, with a particular focus on LLMs and generative AI.

Podstawowe kwalifikacje

- PhD degree in Computer Science, a related field, or equivalent practical experience.
- One or more scientific publication submission(s) for conferences, journals, or public repositories.
- Coding experience in Python, JavaScript, R, Java, or C++.

Preferowane kwalifikacje

- 2 years of coding experience in Python, JavaScript, R, Java, or C++.
- 1 year of experience owning and initiating research agendas.
- Demonstrated expertise in bechmarking, evaluation, and empirical methods.
- Experience with LLMs and GenAI models and methods.

Zakres obowiązków

- Work with the Kaggle Competitions team to expand Kaggle's competitions platform to host and run LLM Benchmarks and community evaluations.
- Collaborate with Google DeepMind Researchers and external researchers to create and host valuable AI evaluations and benchmarks on Kaggle.
- Contribute to the development and deployment of new research methods and algorithms for AI and ML. Mentor and guide junior engineers on the team.
- Publish research papers and present findings at conferences and workshops. Collaborate with engineering, design, and product leadership to improve Kaggle's product for Research priorities.
- Collaborate with other members of the Kaggle Research team to create the next generation of benchmarking and evaluation methods and structures for AI / ML, with a particular focus on LLMs and generative AI.