Research Scientist

Applied Intuition Inc.; Greater London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading AI research organization is seeking a Research Scientist to enhance the web data pipeline. You will focus on improving data quality through scraping and filtering, evaluate pretrained language models, and collaborate with teams to implement infrastructure improvements. Candidates should have at least 3 years' experience in data processing and proficiency in Python or C++. This role offers the chance to work at the forefront of AI research in a diverse and inclusive environment.

Qualifications

• 3 years of experience as an engineer or researcher, such as in software development or graduate studies.
• Experience in building data processing pipelines handling over 100 million examples.
• Familiarity with evaluating pretrained language model performance.

Responsibilities

• Investigate results to identify areas for improvement based on user feedback.
• Develop measurements to evaluate model performance and pipeline effectiveness.
• Set a medium-term improvement agenda with peer feedback.
• Collaborate with partner teams on technology solutions.
• Execute day-to-day coding, experiments, and reviews.

Skills

Self-directed working

Large-scale data processing

Evaluation of LLM performance

Tools

Python

C++

Job description

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

Snapshot

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

The Role

Your job will be to own and lead improvements of meaningful chunks of the web data pipeline. Examples of such chunks are scraping (i.e., transforming raw HTML into clean text data to be used during training, potentially including relevant image data), data filtering (removing/down‑weighting low‑quality content) or adding new data sources (such as historical web crawl data).

Key responsibilities:

• Investigating current results to identify areas for improvement (e.g., based on user feedback or weak eval performance).
• Developing measurements of weakness, either as model eval or data pipeline statistics, to help drive progress.
• Setting out a medium‑term agenda to improve the data pipeline, with feedback from peers and key stakeholders, and convincing others to join your efforts.
• Working with partner teams in GDM (and wider Google) to leverage existing solutions effectively and communicate necessary infrastructure improvements.
• Day‑to‑day execution by coding, running experiments and reviewing contributions.

About You

In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:

• 3 years of experience working as a self‑directed engineer or researcher, e.g., as senior software developer or graduate student.
• Developing large‑scale data (>=100M examples) processing pipelines in Python and/or C++.
• Evaluating and investigating (pretrained) LLM performance.

In addition, the following would be an advantage:

• Filtering data based on heuristic and/or learned signals.
• Working with web data for LLM training, such as cleaning data, removing duplicates, identifying most valuable examples, etc.
• Developing advanced LLM metrics (e.g., execution‑based, using auto‑raters, etc.)