Technical Lead Manager, Applied AI, DeepMind

info_outline XGoogle will be prioritizing applicants who have a current right to work in Singapore, and do not require Google's sponsorship of a visa. Minimum qualifications: • Bachelor's degree in Computer Science, Electrical Engineering, or equivalent practical experience. • 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript). • 7 years of experience leading technical projects and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning), including at least 5 years of experience in a people leadership role overseeing and guiding technical teams. • 5 years of experience in one or more ML fields (e.g., media generation, reinforcement learning, infrastructure, etc.). Preferred qualifications: • Experience with generative AI models and applications and evaluating model performance, analyzing results, and implementing improvements. • Experience with front-end development. • Proven 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). • Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, etc. • Contributions to open-source projects. About the job As a Technical Lead Manager (TLM), you will lead the development and deployment of novel applications, leveraging Google’s generative AI models. You will focus on rapidly developing new features, and working across partner teams to deliver solutions, and maximize impact for Google and top Google customers. You will be instrumental in translating AI research into real-world products, and demonstrating the capabilities of latest generation models. In this role, you will have strong track record of building and shipping software, and leading engineering teams, ideally with some experience in early-stage environments, where you may have contributed to scaling products from initial concept to production. You will be motivated by the opportunity to drive product and business impact, and develop strong relationships with research teams. You will require a strong software engineering foundation, a passion for building and iterating on software products, and proven leadership skills. You will grow in fast-moving environments, and have the proven ability to deliver high-quality code and guide a team to success. You will have experience in end-to-end development, the ability to mentor and develop junior engineers. You will have experience in early-stage or startup environments, where managers take ownership and drive product development from the ground up. 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 • Lead the architecture, design, and development of new scalable products and software applications from 0 to 1, leveraging generative AI models. • Guide the team in rapidly developing new features, iterating quickly based on evaluation results. • Collaborate with researchers and product managers to translate research advancements into tangible product features, and partner with the PM team to qualify and seize new project opportunities. • Mentor and develop team members, fostering a collaborative and high-performing environment. • Oversee the optimization of software performance, ensure the reliability of deployed applications, and advocate best practices for building and deploying generative AI applications.