At a Glance
- Tasks: Prototype and deliver scalable engineering solutions in AI safety and performance.
- Company: Join Google DeepMind, a pioneering AI lab focused on transformative technology.
- Benefits: Diverse learning opportunities, competitive salary, and commitment to ethics and safety.
- Other info: Collaborate with top researchers and contribute to the wider research community.
- Why this job: Make a real impact on global challenges through innovative AI research.
- Qualifications: Bachelor's degree in a technical field and extensive experience in machine learning.
The predicted salary is between 80000 - 100000 € per year.
Minimum qualifications:
- Bachelor's degree in Computer Science, Machine Learning, Mathematics, or a related technical field, or equivalent practical experience.
- 8 years of experience in machine learning engineering or large-scale software systems.
- 3 years of experience in Python programming.
- 3 years of experience with ML frameworks such as JAX, PyTorch, or TensorFlow.
Preferred qualifications:
- Master's degree or PhD in Computer Science, Engineering, or a related field with a focus on Machine Learning.
- Experience working directly on AI safety, adversarial robustness, jailbreak evaluation, or responsible AI research.
- Experience in Python and C++ for high-performance ML library development.
- Experience with adversarial machine learning, red-teaming, AI safety evaluation, or security research.
- Experience building evaluation frameworks, benchmarks, or automated testing pipelines for ML models.
About the Job:
At Google, research-focused Software Engineers are embedded throughout the company, allowing them to set up large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world. From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. You stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority. We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Responsibilities:
- Prototype and deliver scalable engineering solutions rapidly.
- Architect and optimize training and inference pipelines to evaluate the frontier language models.
- Develop post-training strategies to mitigate adversarial risks including jailbreak and prompt injection attacks.
- Collaborate with Research Scientists to translate safety research into implementations and present results to cross-functional stakeholders.
- Build and maintain evaluation infrastructure to systematically track model safety performance.
Research Engineer, Responsibility Engineering, DeepMind employer: Google DeepMind
At Google DeepMind, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. Our Research Engineers are empowered to work on groundbreaking AI projects that have a meaningful impact on society, while benefiting from diverse learning opportunities and career growth pathways. With a commitment to safety, ethics, and public benefit, our interdisciplinary teams thrive in an inclusive environment that values every individual's contribution.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer, Responsibility Engineering, DeepMind
✨Tip Number 1
Network like a pro! Reach out to folks in your field on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and AI safety. We want to see what you can do, so make it easy for us to find your best work.
✨Tip Number 3
Prepare for interviews by practising common technical questions and coding challenges. We recommend using platforms like LeetCode or HackerRank to sharpen your skills. The more prepared you are, the more confident you'll feel!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Research Engineer, Responsibility Engineering, DeepMind
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience in machine learning and Python programming. We want to see how your background aligns with the qualifications listed, so don’t hold back on showcasing your projects and achievements!
Tailor Your Application:Take a moment to customise your application for the Research Engineer role. Use keywords from the job description to demonstrate that you understand what we’re looking for and how you fit into our vision at StudySmarter.
Be Clear and Concise:When writing your application, keep it straightforward and to the point. We appreciate clarity, so avoid jargon and make sure your passion for AI and safety research shines through without unnecessary fluff.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Google DeepMind
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and the ML frameworks mentioned, like JAX, PyTorch, or TensorFlow. Brush up on your knowledge of adversarial machine learning and AI safety, as these are crucial for the role. Being able to discuss specific projects where you've applied these technologies will really impress.
✨Showcase Your Problem-Solving Skills
Prepare to discuss real-world problems you've tackled in your previous roles. Think about how you’ve prototyped solutions or optimised pipelines. Use the STAR method (Situation, Task, Action, Result) to structure your answers, making it easy for interviewers to see your thought process.
✨Collaborate and Communicate
Since the role involves working with cross-functional teams, be ready to share examples of how you’ve collaborated with others. Highlight any experience you have in translating complex research into practical implementations, as this will show your ability to bridge the gap between theory and practice.
✨Stay Current with Research Trends
Familiarise yourself with the latest trends in AI and machine learning, especially around safety and ethics. Mention any relevant papers you’ve read or conferences you’ve attended. This shows your passion for the field and your commitment to staying informed, which is key for a role at a pioneering lab like DeepMind.
