Director of Engineering for CodeAI

Minimum qualifications:

• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
  
• 15 years of professional experience in software engineering
• 10 years of experience in senior leadership roles leading frontier research or applied AI engineering teams
• 3 years of experience experimenting and landing GenAI products
• Experience with concepts such as hill-climbing for quality, fine-tuning/SFT, and agentic systems
• Experience leading and translating frontier AI research into production-grade enterprise GenAI products/agents

Preferred qualifications:

• Master's degree of PhD in Machine Learning, Computer Science, or a related discipline
• Demonstrated impact through leading high-velocity, high-calibre, mission-driven teams, especially those that interface with a frontier research organization
  
• Established presence and thought-leadership in the AI/ML community, demonstrated through landed value by high-impact research or high-value Applied AI products/agents

About the job:

As the Director of Engineering for CodeAI, you will partner deeply with DeepMind (GDM) to lead the technical strategy for bringing frontier AI capabilities to enterprise scale product development workflows.

The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.

Responsibilities:

• Serve as a high-bandwidth partner for research teams in DeepMind in identifying critical gaps in core intelligence pieces - LLM, harness and adjacent capabilities (memory, context engineering, recursive self-improvement loops), activating and successfully executing on research and development work to improve LLMs+ adjacent pieces for Google scale critical user journeys.
  
• Translate frontier capabilities into high-value product capabilities that professionals love and set up feedback flywheel to extract and upstream rich expert signal to GDM (e.g., high-quality, high-complexity datasets that can unlock model hill-climbing for higher level of autonomy for complex tasks).
  
• Build, scale, and inspire an organization of frontier AI-native engineering by maintaining focus on fast pace of experimentation/iteration and identifying the biggest blockers to getting to AGI, partnering with GDM to land research and value breakthroughs at Google scale, through first-principles thinking.