Manager, Applied AI Engineering, DeepMind
You will be a passionate machine learning engineering leader with a drive to build innovative products and a desire to work at the forefront of AI.
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.
- 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.
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.