Google DeepMind is looking to build AI responsibly to benefit humanity by focusing on a range of research projects within the Responsibility portfolio.
Requirements
- Deep understanding and hands-on experience with state-of-the-art Machine Learning techniques, particularly in Deep Learning and Large Language Models (LLMs).
- Strong programming skills, preferably in Python, and experience with common ML frameworks (e.g., TensorFlow, PyTorch, JAX).
- Experience with large-scale data processing and distributed systems.
- Publications or contributions to the machine learning research community.
- Experience working in a fast-paced, research-driven environment.
- Familiarity with the sociotechnical aspects of AI systems.
- Experience working in AI ethics, safety, or responsibility research.
Responsibilities
- Lead, mentor, and grow a team of Research Engineers.
- Define and drive the technical strategy and roadmap for research projects within the Responsibility portfolio.
- Oversee the design, development, and implementation of robust and scalable ML systems and tools.
- Ensure projects are well-defined, resourced, and executed efficiently to meet research and organizational objectives.
- Collaborate with cross-functional teams (e.g., Research, Product, Policy) to identify and address key challenges in responsible AI.
- Communicate complex technical concepts, project statuses, risks, and opportunities clearly and concisely to various stakeholders, including researchers, engineers, and executive leadership.
- Stay abreast of the latest advancements in AI, machine learning, LLMs, and AI ethics and safety.
Other
- Bachelor's degree in Computer Science, Engineering, Machine Learning, or a related technical field, or equivalent practical experience.
- 7+ years of experience in a technical leadership role, with at least 3 years of experience managing engineering teams.
- Excellent communication and interpersonal skills, with the ability to explain technical concepts to both technical and non-technical audiences, including executive leadership.
- Ability to work flexibly across multiple projects and adapt to evolving priorities.
- Master's or PhD in Computer Science, Machine Learning, or a related field.
