GenAI Technical Engagement Lead, DeepMind

Google•Mountain View, CA

9d•$277,000 - $308,000

About The Position

The GenAI Technical Engagement team within Google DeepMind is responsible for translating complex technical model-development roadmaps to support frontier AI public policy and regulatory efforts, working collaboratively with cross-functional partners at Google to advance actionable advocacy routes to harmonize public policy efforts with frontier model development and deployment. In this role, you will work at the intersection of research, engineering, public policy, regulatory, product, and operational teams to anticipate emerging regulations (such as those related to AI model development, training data, copyright, safety, and security) and harmonize them with Google DeepMind’s technical and model roadmap. As the technical subject matter expert, you’ll collaborate closely with public policy, regulatory, and frontier AI model development teams to identify key technical needs and goals, steer advocacy strategies early, co-design policy playbooks and develop external responses to emerging regulations in ways that meet Google’s model development and deployment objectives. 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $277000 - $308000 (USD) + 25% bonus target + equity + benefits Learn more about benefits at Google [https://www.google.com/about/careers/applications/benefits/].

Requirements

Bachelor’s degree in Computer Science, Data Science, Machine Learning, Law, Public Policy or equivalent practical experience.
8 years of experience in either technical product/engineering/researcher roles, or policy/regulatory-facing roles.
Experience with foundational models development lifecycles, model capabilities, training data architectures, and model evaluation and deployment.
Experience working with cross-functional teams in technical engineering/research organizations.

Nice To Haves

Advanced degree in a technical discipline or public policy.
Experience designing, leading, or implementing high-stakes, complex projects from the ground up.
Experience engaging with regulators and policymakers about the issues at the heart of AI model development, including data use, model architecture, safety, security, and compute.
Track record of excellent contributions to AI model development as a technologist, researcher, public policy manager, or communications specialist.

Responsibilities

Bridge the gap between AI development and regulation by translating complex AI AI model development plans into actionable public policy responses that you co-design with Google teams.
Steer cross-functional advocacy. Partner directly with Google cross-functional teams who are managing public policy responses to ensure advocacy and policy frameworks and playbooks evolve in lockstep with model development roadmaps.
Advise senior management on the status of emerging, novel regulatory developments, including impact, and recommended solutions based on model development plans.
Serve as a technical subject matter expert for policy and regulatory teams, providing guidance to internal teams and public policymakers on AI concepts such as training data disclosures and model transparency.
Establish metrics to measure the success and impact of advocacy campaigns on model development roadmap, generating actionable insights for campaign optimization.