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- Tasks: Bridge research and production for cutting-edge AI models at Google DeepMind.
- Company: Join the innovative team at Google DeepMind, leading in AI advancements.
- Benefits: Competitive salary, health benefits, remote work options, and growth opportunities.
- Why this job: Make a real impact on AI deployment and enhance user experiences globally.
- Qualifications: Proficiency in C++ or Python, with experience in production environments.
- Other info: Dynamic workplace focused on collaboration and continuous learning.
The predicted salary is between 36000 - 60000 ÂŁ per year.
We are searching for a talented engineer passionate about bridging the gap between research and production for cutting‑edge AI models. In this role, you will play a key part in accelerating the hyperscale, low‑latency deployment of Google DeepMind's Gemini models across text, audio, image and video, and onto various product surfaces within Google. Your work will involve productionizing, optimizing and serving models, collaborating with research teams to ensure models are production‑ready, and identifying ways to streamline the entire research‑to‑production process. This is a unique opportunity to directly impact the speed and efficiency with which Google delivers innovative AI‑powered products and features to users.
About Us: 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: In this role, you will be at the forefront of bringing cutting‑edge AI research to life. You will work directly with researchers and engineers to optimize and deploy multimodal large language models (LLMs) like Gemini onto Google's serving infrastructure, impacting users across a diverse range of applications and accelerating research projects. This involves a blend of technical expertise and collaborative problem‑solving to ensure both efficiency and quality throughout the entire LLM development and deployment lifecycle.
- Key responsibilities:
- Bridge the infrastructure gap between research and production: Collaborate closely with research teams to understand next generation modeling approaches, ensuring they are designed and implemented with production considerations in mind.
- Optimize the serving environment: Contribute to and collaborate with other infrastructure teams to deliver serving infrastructure that is designed for maximum efficiency and performance, addressing bottlenecks in speed and scale.
- Design and implement novel, high‑performance serving techniques: e.g., continuous batching, speculative decoding, request‑level scheduling, for maximum throughput and efficiency.
- Streamline the deployment process: Identify opportunities to automate tasks, eliminate redundancies, and improve the overall velocity of model releases.
- Develop expertise in model serving technologies: Gain a deep understanding of serving frameworks, preprocessing pipelines, caching mechanisms, and other relevant technologies.
- Identify the best hardware setup for deploying a diverse set of models: Conduct deep performance profiling and improve efficiency of ML model serving on hardware accelerators.
- Stay informed on industry trends: Continuously learn about new technologies and best practices in the field of AI research and deployment.
This is a chance to make a real difference in the way Google develops and deploys AI, directly impacting the speed and effectiveness with which we deliver innovative solutions to users.
About You: In order to set you up for success as a Software Engineer at Google DeepMind, we look for the following skills and experience:
- Interpersonal skills, such as discussing technical ideas effectively with colleagues and collaborating with other roles.
- Excellent knowledge of either C++ or Python.
- Experience with deployment in production environments.
- Experience with developing serving infrastructure.
- Familiarity or experience with optimisation of distributed ML systems.
- Familiarity with modern HW accelerators (GPU / TPU).
Deadline - 12th December 2025
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.
Software Engineer, Gemini Deployment, London employer: Google DeepMind
Contact Detail:
Google DeepMind Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Software Engineer, Gemini Deployment, London
✨Tip Number 1
Network like a pro! Reach out to current employees at Google DeepMind on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing the Software Engineer role. Personal connections can make a huge difference!
✨Tip Number 2
Show off your skills! If you’ve worked on projects related to AI, deployment, or optimisation, create a portfolio or GitHub repository showcasing your work. This gives you a chance to demonstrate your expertise beyond just a CV.
✨Tip Number 3
Prepare for technical interviews by practising coding challenges and system design problems. Use platforms like LeetCode or HackerRank to sharpen your skills. Remember, they’ll want to see how you think through problems, so articulate your thought process!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in being part of the Google DeepMind team. Don’t forget to tailor your application to highlight your relevant experience!
We think you need these skills to ace Software Engineer, Gemini Deployment, London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the role. Highlight your experience with C++ or Python, and any work you've done in production environments. We want to see how you can bridge the gap between research and production!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a great fit for the Software Engineer role. Don’t forget to mention your collaborative spirit and problem-solving skills!
Showcase Relevant Projects: If you've worked on projects involving model serving or optimisation of distributed ML systems, make sure to include them. We love seeing real-world applications of your skills, so don’t hold back on the details!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our team at Google DeepMind!
How to prepare for a job interview at Google DeepMind
✨Know Your Tech Inside Out
Make sure you brush up on your knowledge of C++ or Python, as well as any deployment experience you have. Be ready to discuss specific projects where you've optimised serving infrastructure or worked with distributed ML systems. This will show that you're not just familiar with the tech, but that you can apply it effectively.
✨Show Off Your Collaboration Skills
Since this role involves working closely with research teams, be prepared to share examples of how you've successfully collaborated in the past. Highlight situations where you’ve discussed technical ideas and solved problems together. This will demonstrate your interpersonal skills and ability to bridge gaps between different teams.
✨Understand the Deployment Process
Familiarise yourself with the entire lifecycle of model deployment, from research to production. Be ready to discuss how you would streamline processes and automate tasks. Showing that you can think critically about improving efficiency will set you apart from other candidates.
✨Stay Current with Industry Trends
Keep yourself updated on the latest technologies and best practices in AI research and deployment. Mention any recent advancements or tools you’ve learned about, and how they could apply to the role. This shows your passion for the field and your commitment to continuous learning.
