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.
Preferred qualifications:
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- Experience in full-stack development, including in programming languages like Python, Java, C++, web frameworks, and cloud platforms.
- Experience with Generative AI technologies and their associated risks and opportunities.
- Knowledge of Trust and Safety, Content Safety, or Responsible AI domains, with an understanding of adversarial dynamics, content moderation challenges, or similar safety-adjacent fields.
- Knowledge of ML algorithms, including supervised and unsupervised learning, deep learning, reinforcement learning, and generative AI.
- Knowledge of API design, database technologies, and front-end development principles.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
Content Safety Applications provides safety solutions that protect Google’s users from exposure to offensive, sensitive, or potentially harmful content.
This team keeps users safe at scale, but also unblock and accelerate Google's innovative product launches by providing high-quality, low-cost implementations of company-wide standards for Responsible AI, making them available off-the-shelf for any product or feature team to use.
In this role, you will combine unique subject matter expertise in the safety domain along with the production-grade experience in serving infrastructure and product integrations to protect users from harm, maintain Google’s competitiveness in the area of online safety across the industry, and do your part to make the internet as a whole safer for society as a whole.
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
- Lead the full lifecycle of projects that protect Google's users and business-critical products, including the latest generative AI experiences. Responsible for designing, building, and scaling the next generation of the threat intelligence and content safety solutions.
- Accountable for delivering high-quality, future-proof and performing infrastructure, unlocking new opportunities for the business. Join the existing team in Dublin, contribute to the success of Content Safety engineers working in Agents, Classifiers and Responsible AI efforts.
- Lead the deployment of Agents, or AI-based protections/classifiers, from initial problem definition and data acquisition through to model development, evaluation, deployment, and long-term maintenance.
- Collaborate effectively with cross-functional teams of data scientists, software engineers, product managers, and business stakeholders to deliver impactful safety solutions.
