トレンド企業

Google DeepMind
Google DeepMind

Organizing the world's information and making it universally accessible.

Research Scientist, Multimodal Generative AI, Google DeepMind

職種データサイエンス
経験ミドル級
勤務地Singapore
勤務オンサイト
雇用正社員
掲載2ヶ月前
応募する

必須スキル

Python

PyTorch

TensorFlow

Machine Learning

Job Description

Our team works on developing state-of-the-art methods for AI generative media models, with a particular focus on culturally-adapted image and video generation.

At Google Deep Mind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our special interdisciplinary team combines the best techniques from deep learning, reinforcement learning, and systems neuroscience to build general-purpose learning algorithms. We have already made a number of high-profile breakthroughs towards building artificial general intelligence, and we have all the ingredients in place to make further significant progress over the coming year!

Research Scientists lead our efforts in developing novel tools, infrastructure, and algorithms towards the end goal of solving and building Artificial General Intelligence.

Having pioneered research in the world's leading academic and industrial labs, Ph Ds, post-docs, or professorships, Research Scientists join Google Deep Mind to work collaboratively within and across Research fields. They are expected to work with teams on large scale AI, and develop solutions to fundamental questions in machine learning and AI.

Drawing on expertise from a variety of disciplines including deep learning, computer vision, language modeling, and advanced generative architectures, our Research Scientists are at the forefront of groundbreaking research.

Job responsibilities

Design, rapidly implement, and rigorously evaluate cutting-edge deep learning algorithms and data curation for multimodal generative AI, with a particular emphasis on culturally-adapted image and video synthesis.

Report and present research findings and developments clearly and efficiently both internally and externally, verbally and in writing.

Suggest and engage in team collaborations to meet ambitious research goals, while also driving significant individual contributions.

Work in collaboration with our Ethics and Governance teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

Minimum qualifications

  • PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or equivalent practical experience.

  • 2+ years of relevant experience in deep learning research and development, particularly in generative AI and related to image and video synthesis. This includes diffusion models and autoregressive generative models.

  • Experience in software development with one or more programming languages (e.g., Python) and deep learning frameworks (e.g., Jax, Tensor Flow, Py Torch), with a track record of building high-quality research prototypes and systems.

Preferred qualifications

  • Demonstrated experience in large-scale training of multimodal generative models.

  • A track record of research or engineering achievements, including publications in peer-reviewed conferences or journals.

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Google DeepMindについて

Google DeepMind

DeepMind Technologies Limited, trading as Google DeepMind or simply DeepMind, is a British-American artificial intelligence research laboratory which serves as a subsidiary of Alphabet Inc.

1,001-5,000

従業員数

London

本社所在地

レビュー

10件のレビュー

3.8

10件のレビュー

ワークライフバランス

3.8

報酬

4.2

企業文化

3.5

キャリア

4.0

経営陣

2.8

68%

知人への推奨率

良い点

Smart and brilliant colleagues

Good compensation and benefits

Work flexibility and remote options

改善点

Poor management and leadership issues

Bureaucracy and slow processes

Constantly changing priorities and goals

面接レビュー

レビュー5件

難易度

3.0

/ 5

期間

21-35週間

内定率

60%

体験

ポジティブ 60%

普通 40%

ネガティブ 0%

面接プロセス

1

Application Review

2

Phone Screen/Online Assessment

3

Technical Interview

4

Team Matching Interview

5

Offer

よくある質問

Coding/Algorithm

Technical Knowledge

Behavioral/STAR

Research Experience

System Design