Software Engineer, Content Safety

MINIMUM QUALIFICATIONS:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software programming in Python, Java, or C++.
• 1 year of experience in a core ML domain, such as generative AI, Natural
  Language Processing (NLP), computer vision, speech/audio, reinforcement
  learning, recommendation systems, or ML infrastructure.
• 1 year of experience with ML infrastructure (e.g., model training, model
  inference, model deployment, model evaluation, optimization, data processing,
  debugging).

PREFERRED QUALIFICATIONS:

• Experience in safety-adjacent domains, including factuality, product policy,
  or broader responsible AI frameworks.
• Experience managing safety for UGC or GenAI products, with a deep
  understanding of adversarial incentives, abuse vectors, and distribution
  dynamics like virality.
• Experience designing and deploying global-scale defensive architectures and
  pipelines capable of meeting rigorous Service Level Objectives (SLOs).
• Demonstrated accountability for managing technical debt, reducing bug counts,
  and mitigating SLO breaches to maintain high operational standards.
• Solid high-level understanding of Machine Learning and Large Language Model
  (LLM) architecture, specifically transformers, activations, and the
  requirements for efficient, large-scale training and deployment.

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.

Our mission in the Content Safety organization is to protect Google’s users and
the internet as a whole from exposure to offensive, sensitive or potentially
harmful content. We achieve this by contributing directly to our foundational
models and collaborating closely with DeepMind.

Beyond keeping users safe at scale, we also play a key role in accelerating
Google's product launches by providing product teams with the right tools to
explore new ideas and products following our Responsible AI principles. Our team
combines unique subject matter expertise in the content safety domain, ML and
high-throughput infrastructure.

In this role, you will keep society safer. You will work on problems like
transformer architecture, and find comfort in an ever-changing landscape,
anticipating threats that don't exist yet.

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:

• Design, build, and scale content safety systems including classifiers, vector
  databases, and multimodal models to protect business-critical products and
  GenAI experiences.
• Develop and maintain production-grade distributed systems and content
  processing pipelines optimized for high throughput and reliability across
  server-side and on-device environments.
• Model training, evaluation, and productionization workflows, incorporating
  feedback loops and automation to continuously improve model quality and
  performance.
• Implement agentic workflows and advanced heuristics for deep threat
  understanding, enabling the proactive detection of complex abuse patterns.
• Drive agile engineering efforts to identify and mitigate novel abuse
  patterns, ensuring Google’s products remain engaged and safe in a shifting
  threat landscape.