Research Engineer, Serving Quality, DeepMind

In this role, you will work cross-functionally with researchers, engineers and operations on live experiment set up, user and model behavior analysis, and metrics improvements to ensure that we have the best quality of data for GenAI model and product developments. This role also works with the modeling teams closely to provide user signals feedback and insights on improving our frontier models.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

Responsibilities:

• Design and develop metrics that measure model performance, and detect anomaly events on serving stack (such looping, leakage and so on).
• Discover novel serving or modeling quality issues, as well as anomaly events through large-scale real user data mining.
• Work cross-functionally on logging, serving, and management to quantify the improvements.
• Contribute to reporting, analytical infrastructure, model testing framework including replay, evals and dashboarding.
• Provide insights to improve our frontier models serving quality and improve user experiences. 

Minimum qualifications:

• Bachelor's degree in Computer Science, Mathematics, Statistics, Machine Learning, a related field, or equivalent practical experience.
• Experience with Python or C++.
• Experience with machine learning and statistics.

Preferred qualifications:

• Experience in software engineering and working on large-scale ML projects.
• Experience working on projects from proof-of-concept through to implementation.
• Experience in experiment analysis.
• Experience with TensorFlow or similar ML frameworks (e.g. JAX).
• Experience conducting applied research to improve the quality and training/serving efficiency of large transformer-based models.
• Familiarity with LE and Rasta.