As part of the team, you will have opportunities to participate in advancing AI for Chip Design. In this role, you will take on the huge challenge of automating the chip design workflow, which can transform the EDA industry. Your opportunity is to build such capabilities, and pioneer next-generation products in collaboration with teams spanning major Product Areas.
In this role, you will have many areas where you can contribute, including but not limited to bringing the most advanced ML models and technologies to chip design, enabling efficiency across the entire AI learning stack, scaling and hardening research prototypes into tools that serve chip design across Google. You will engage and work in a fast-paced, rapidly shifting environment, demonstrating flexibility and the ability to bring clarity in ambiguous problem spaces.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.
- Architect and maintain custom multi-agent orchestration systems; collaborate with research engineers to automate design tools, leverage AI for architectural exploration, and narrow search spaces.
- Perform code reviews to enforce best practices, optimize performance, build modular software packaging, and improve efficiency across the AI learning stack.
- Drive project timelines, deliverables, and priorities; guide cross-functional teams; and lead or participate in design reviews to select optimal solutions.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 5 years of experience with Machine Learning, Generative AI, Large Language Models (LLMs), AI Agents, or ML infrastructure.
- 3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- 5 years of experience with data structures and algorithms.
- 5 years of experience with AI/ML Infrastructure (e.g., GPUs, GKE, EKS, Kubernetes, Kubeflow).