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职位Google DeepMind

Staff Software Engineer, Applied AI

Google DeepMind

Staff Software Engineer, Applied AI

Google DeepMind

Singapore

·

On-site

·

Full-time

·

1mo ago

必备技能

Python

AWS

Azure

Job Description

We are seeking a Staff Applied AI Engineer to lead the development and deployment of novel applications, leveraging Google’s generative AI models. This role focuses on rapidly developing new features, and working across partner teams to deliver solutions, and maximize impact for Google & top Google customers. You will be instrumental in translating cutting-edge AI research into real-world products, and demonstrating the capabilities of latest generation models. We are looking for engineers with a strong track record of building and shipping software, ideally with experience in early-stage environments where they have contributed to scaling products from initial concept to production. The ideal candidate will be motivated by the opportunity to drive product & business impact.

This role requires a strong software engineering foundation and a passion for building and iterating on software products, using state-of-the-art GenAI tools. We are looking for engineers who thrive in fast-paced environments and have a proven ability to deliver high-quality code. Experience in prototyping, building, and scaling software is essential. We particularly value experience in early-stage or startup environments, where engineers take ownership and drive product development from the ground up.

Job responsibilities

  • Design and develop scalable software applications leveraging generative AI models.

  • Rapidly prototype new features and iterate based on evaluation results.

  • Collaborate with researchers and product managers to translate research advancements into tangible product features.

  • Optimize software performance and ensure reliability of deployed applications.

  • Contribute to the development of best practices for building and deploying generative AI applications.

  • Lead the architecture and development of new products & features from 0 to 1.

Minimum qualifications:

  • A bachelor degree in computer science, electrical engineering or equivalent experience.

  • 8 years of experience in software development with one or more programming languages (e.g., Python) and with data structures/algorithms.

  • 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

  • 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, evaluation, data processing, debugging, and fine-tuning), with expertise in areas such as media generation or reinforcement learning.

Preferred qualifications:

  • Experience with generative AI models and applications.

  • Contributions to open-source projects.

  • Experience with front end development.

  • Experience in rapidly developing and shipping software products in a fast-paced, customer-facing startup-like environment, demonstrating adaptability to changing priorities.

  • Experience with cloud computing platforms and infrastructure (e.g., Google Cloud Platform, AWS, Azure).

  • Experience with machine learning frameworks and libraries such as Tensor Flow, Py Torch, Hugging Face, etc.

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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