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职位Google DeepMind

Research Scientist, Audio

Google DeepMind

Research Scientist, Audio

Google DeepMind

New York City, New York, US

·

On-site

·

Full-time

·

1d ago

Snapshot

Artificial Intelligence could be one of humanity’s most useful inventions. At Google Deep Mind, 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.

About Us

Members of the team are a group of researchers with core contributions into the Gemini Audio pillar. Specifically, the team works on audio and audio-visual understanding and generation tasks using large language models. Research includes, but is not limited to, better acoustic representations and tokenizers, better generation modeling, and audio and audio-visual open-ended tasks such as dialog, TTS, question-answering and dubbing.

The Role

Research Scientists at Google Deep Mind lead our efforts in developing novel algorithmic architecture towards the end goal of solving and building Artificial General Intelligence.

In this role, responsibilities will include making key contributions into the latest research developed in the Gemini audio pillar, such as:

Key responsibilities:

  • Data: Unlocking new audio to X capabilities within the model, both in pre-training and post-training.
  • Models: Improving quality of models for understanding and generation. This includes research to improve our tokenizers, better techniques for generation quality, and looking at joint audio and visual representations.
  • Evals: Better evaluation methods (human, auto raters, automated metrics) to measure quality of open-ended tasks.

About You

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

  • PhD in Computer Science, Computer Vision, Speech Processing, or Machine Learning related field.
  • Experience working with LLMs.
  • Audio or video understanding and/or generation experience.

In addition, the following would be an advantage:

  • A proven track record of research and publications in some of the following areas: audio generation, video generation, LLMs
  • A real passion for AI!

The US base salary range for this full-time position is between $147,000 - $211,000 + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.

At Google Deep Mind, 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.

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关于Google DeepMind

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

员工数

London

总部位置

评价

3.8

10条评价

工作生活平衡

3.8

薪酬

4.2

企业文化

3.5

职业发展

4.0

管理层

2.8

68%

推荐给朋友

优点

Smart and brilliant colleagues

Good compensation and benefits

Work flexibility and remote options

缺点

Poor management and leadership issues

Bureaucracy and slow processes

Constantly changing priorities and goals

面试经验

5次面试

难度

3.0

/ 5

时长

21-35周

录用率

60%

体验

正面 60%

中性 40%

负面 0%

面试流程

1

Application Review

2

Phone Screen/Online Assessment

3

Technical Interview

4

Team Matching Interview

5

Offer

常见问题

Coding/Algorithm

Technical Knowledge

Behavioral/STAR

Research Experience

System Design