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For Companies At a Glance
- Tasks: Join us to develop and improve large-scale multimodal models and data pipelines.
- Company: Be part of Google DeepMind, a leader in AI innovation for public benefit.
- Benefits: Enjoy a diverse workplace with equal opportunities and support for all backgrounds.
- Why this job: Make an impact in AI while collaborating with top scientists and engineers.
- Qualifications: Proven experience in machine learning, coding, and large-scale system design required.
- Other info: Applications close on January 20, 2024. Don't miss out!
The predicted salary is between 43200 - 72000 £ per year.
At Google DeepMind, 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.
Snapshot
Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, 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.
The Role
We’re looking for a Research Engineer with exceptional engineering skills and understanding of large-scale neural network training and data processing, as well as a strong working knowledge of machine learning experimentation.
Key responsibilities:
- Develop, maintain, and improve large scale multimodal models and data pipelines (used for training and evaluation of models).
- Collaborate with team members to develop and implement new methods for evaluating and improving multi-modal generative models, particularly at the post-training stage.
- Develop and maintain pipelines to collect human data.
About You
In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:
- Proven experience of building codebases that support machine learning at scale. You are familiar with ML / scientific libraries (e.g. JAX, TensorFlow, PyTorch, Numpy, Pandas), distributed computation, and large scale system design.
- Knowledge of machine learning and statistics.
In addition, the following would be an advantage:
- MSc or PhD/DPhil degree in computer science, physics, mathematics, applied stats, machine learning, or similar experience working in industry.
- Experience with LLMs, VLMs, and/or diffusion models training and inference.
- Experience working in industry on team projects from early (proof-of-concept) to late stages (implementation and evaluation).
Applications close Monday 20th January 2024.
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Research Engineer, Language employer: Google DeepMind
Contact Detail:
Google DeepMind Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Engineer, Language
✨Tip Number 1
Make sure to showcase your experience with large-scale neural network training and data processing in your conversations. Highlight specific projects where you've successfully implemented machine learning models, as this will resonate well with the team at Google DeepMind.
✨Tip Number 2
Familiarize yourself with the latest advancements in multimodal models and generative models. Being able to discuss recent research or breakthroughs in these areas during your interactions can demonstrate your passion and knowledge, making you a more attractive candidate.
✨Tip Number 3
Network with current or former employees of Google DeepMind if possible. Engaging in conversations about their experiences can provide valuable insights into the company culture and expectations, which you can leverage during your application process.
✨Tip Number 4
Prepare to discuss your familiarity with ML libraries like JAX, TensorFlow, and PyTorch. Be ready to explain how you've used these tools in past projects, as practical knowledge in these areas is crucial for the Research Engineer role.
We think you need these skills to ace Research Engineer, Language
Some tips for your application 🫡
Understand the Role: Take the time to thoroughly read the job description for the Research Engineer position at Google DeepMind. Make sure you understand the key responsibilities and required skills, as this will help you tailor your application.
Highlight Relevant Experience: In your CV and cover letter, emphasize your experience with machine learning, large-scale neural networks, and any relevant projects you've worked on. Be specific about the technologies and libraries you have used, such as JAX, TensorFlow, or PyTorch.
Showcase Collaboration Skills: Since collaboration is a key aspect of the role, include examples in your application that demonstrate your ability to work effectively in teams. Mention any projects where you collaborated with others to achieve common goals.
Prepare a Strong Cover Letter: Craft a compelling cover letter that not only outlines your qualifications but also expresses your passion for artificial intelligence and its potential for public benefit. Make sure to connect your personal values with those of Google DeepMind.
How to prepare for a job interview at Google DeepMind
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning libraries like JAX, TensorFlow, and PyTorch. Highlight specific projects where you built codebases that supported machine learning at scale.
✨Demonstrate Collaboration Experience
Since collaboration is key in this role, share examples of how you've worked with team members on projects. Discuss your contributions from the early proof-of-concept stages to implementation and evaluation.
✨Discuss Your Understanding of Multimodal Models
Make sure to articulate your knowledge of multimodal models and data pipelines. Be ready to explain how you would approach developing and improving these systems, especially in the context of post-training evaluation.
✨Prepare for Ethical Considerations
Given the emphasis on safety and ethics at Google DeepMind, be prepared to discuss how you incorporate ethical considerations into your work. Share any relevant experiences or thoughts on responsible AI development.
