Software Engineer II, AI/ML, Account Threats

3 Months ago • 1-2 Years • Artificial Intelligence

Job Summary

Job Description

As a Software Engineer II, AI/ML, Account Threats, you'll be responsible for designing, building, optimizing, and operating the Machine Learning (ML) ecosystem that powers transformer-based account activity models. You'll implement ML pipelines for seamless experimentation, feature engineering, model training, validation, and deployment, accelerating the time-to-production for new threat detection models. You'll also design and implement model metrics and benchmarks aiding in explainability of model behavior. This role requires you to analyze models drift and identify opportunities for refinement, ensuring sustained models accuracy and effectiveness. You'll collaborate closely with our partners, including Trust and Safety to ensure a seamless adoption in our daily threats fighting operations.
Must have:
  • Bachelor's degree or equivalent practical experience
  • 1 year of experience with software development in one or more programming languages
  • 1 year of experience with data structures or algorithms
Good to have:
  • Master's degree in Computer Science, Machine Learning, a related field, or equivalent practical experience
  • 2 years of experience developing and deploying machine learning models
  • 2 years of experience with large-scale distributed systems
  • Experience in adversarial space, fraud detection, or other anti-abuse domains
  • Familiarity with TensorFlow or similar frameworks

Job Details

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 1 year of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
  • 1 year of experience with data structures or algorithms.

Preferred qualifications:

  • Master's degree in Computer Science, Machine Learning, a related field, or equivalent practical experience.
  • 2 years of experience developing and deploying machine learning models, especially deep learning or natural language processing techniques.
  • 2 years of experience with large-scale distributed systems in the context of machine learning infrastructure or applications.
  • Experience in adversarial space, fraud detection, or other anti-abuse domains.
  • Familiarity with TensorFlow or similar frameworks.

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.

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, optimize, and operate the Machine Learning (ML) ecosystem that powers transformer-based account activity models.
  • Implement ML pipelines for seamless experimentation, feature engineering, model training, validation, and deployment, accelerating the time-to-production for new threat detection models.
  • Design and implement model metrics and benchmarks aiding in explainability of model behavior.
  • Analyze models drift and identify opportunities for refinement, ensuring sustained models accuracy and effectiveness.
  • Collaborate closely with our partners, including Trust and Safety to ensure a seamless adoption in our daily threats fighting operations.

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