Manager, Applied AI Engineering, DeepMind

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

In this role, you will have experience in low-level computer systems, computer architecture, embedded systems, and kernel development. Key development areas will include the Linux (Android) kernel, device drivers, embedded firmware, Android userspace daemons, performance analysis, and Linux power management. You will collaborate across multidisciplinary domains through every stage of development—from schematic reviews to final user experience. You will also have a history of open-source Linux contributions. For decades, the computing revolution has reshaped our world driven by breakthroughs in compute, connectivity, mobile, and now, AI. Google's XR team is at the forefront of the next major leap – the convergence of AI and XR. This is more than just new devices – it's about reimagining how we interact with the world around us. We're building a future where lightweight XR devices like smart glasses and headsets pair with helpful AI to augment human intelligence, offering personalized, conversational, and contextually aware experiences.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google. Responsibilities

• Lead a team in the design, development, and deployment of scalable generative AI applications and advocating industry best practices.

• Lead the team through the rapid development of new features, iterating based on evaluation results while mentoring members to cultivate a collaborative and high-performing environment.

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

• Oversee the optimization of software performance and ensure the reliability of deployed applications.

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

Qualifications Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.

• 8 years of software development experience, including system design, data structures, and algorithms.

• 7 years of experience leading technical project strategy, ML design, and optimizing industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

• 5 years of experience in a technical leadership role; overseeing projects, with 5 years of experience in a people management, supervision/team leadership role.

Preferred qualifications:

• 5 years of experience with one or more of the following: media generation, reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.

• Experience with generative AI research or applications.

• Experience evaluating model performance, analyzing results, and implementing improvements.

• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Hugging Face, etc.

• Experience in developing and shipping software products rapidly.

• Contributions to open-source projects.