Senior ML Personalization Engineer (Recommender & LLMs) Bonus

DeepMind Technologies Limited; San Francisco (CA); On-site; USD 180,000 - 300,000; Full time

Benefits offered by this job

Bonus

Equity

Benefits

Job summary

DeepMind Technologies Limited in San Francisco is seeking an experienced software engineer to design and build robust personalization features across the Gemini App stack. You will use user feedback, logs, and data to identify quality issues and opportunities, improving personalization via fine-tuning and prompting.

Collaborate with research, engineering, product, and program teams to develop automated and human-in-the-loop evaluation methods for continuous improvement.

Qualifications

• Requires a bachelor degree or equivalent practical experience.
• At least five years in software development with testing or product launches.
• Experience in software design/architecture and machine learning, recommender systems, deep learning, or personalization.
• Python or C++ experience is required; master's or PhD preferred.

Responsibilities

• Design and build robust, scalable personalization features across the Gemini App stack.
• Leverage user feedback, logs, and evaluation data to identify quality issues and opportunities.
• Improve personalization through fine-tuning or prompting; develop automated and human-in-the-loop evaluation methods.
• Collaborate with research, engineering, product, and program teams.

Skills

Software Development

Machine Learning

Personalization

Recommender Systems

Deep Learning

Python

C++

Software Testing

Software Design

Software Architecture

Data Analysis

Fine-Tuning

Prompting

Evaluation Techniques

Natural Language Processing

Large Language Models

Education

Bachelor's degree or equivalent practical experience

Master's degree or PhD (preferred)

Tools

Python

C++

NLP

LLMs

Job description

DeepMind Technologies Limited in San Francisco is seeking an experienced software engineer to design and build robust personalization features across the Gemini App stack. You will use user feedback, logs, and data to identify quality issues and opportunities, improving personalization via fine-tuning and prompting.

Collaborate with research, engineering, product, and program teams to develop automated and human-in-the-loop evaluation methods for continuous improvement.