Senior AI/ML Software Engineer, YouTube Knowledge

As a Technical Solutions Consultant, you will be responsible for the technical relationship of our largest advertising clients and/or product partners. You will lead cross-functional teams in Engineering, Sales and Product Management to leverage emerging technologies for our external clients/partners. From concept design and testing to data analysis and support, you will oversee the technical execution and business operations of Google's online advertising platforms and/or product partnerships.

You will be able to balance business and partner needs with technical constraints, develop innovative, cutting edge solutions and act as a partner and consultant to those you are working with. You will also be able to build tools and automate products, oversee the technical execution and business operations of Google's partnerships, as well as develop product strategy and prioritize projects and resources.

Operating as a critical subject matter expert within the central Enterprise Product Architecture team, this leader will act as the strategic and technical anchor for our Enterprise Architecture governance and cross-pillar rationalization strategies. You will advocate the enterprise-wide introduction and adoption of Process Mining, establishing the horizontal frameworks, methodologies, and tooling needed to unlock data-driven workflow optimization across Corporate Engineering.

You will also standardize architectural frameworks, actively drive EAB (Enterprise Architecture Board) decision-making and gate-review processes, and establish our Master EA Catalog. You will author core reference architectures and blueprints including domains like Data and AI. Working closely with technical leads and engineering squads across all pillars, you will centralize technical artifacts, unblock architectural dependencies, and directly orchestrate rationalization strategies to simplify and optimize our enterprise landscape. 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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $183000 - $266000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google. Responsibilities

• Write and test product or system development code.

• Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).

• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.

• Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.

• Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.

Qualifications Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.

• 5 years of experience with software development in one or more programming languages.

• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.

• 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 ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

• Master's degree or PhD in Computer Science, or a related technical field.

• 5 years of experience with data structures and algorithms.

• 1 year of experience in a technical leadership role.

• Experience developing accessible technologies.