Senior Software Engineer, ML for Android Malware Detection

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 5 years of experience with software development in one or more programming languages (C++).
• 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 ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
  
• 5 years of experience with data structures/algorithms.
• 1 year of experience in a technical leadership role.
  
• Experience developing accessible technologies.
  

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 Arc-AI team is part of the Mobile Threat Analysis Platform group in the AASE (Android App Safety Engineering) organization. Our mission is to provide solutions for large-scale detection and enforcement against abusive code developed by adapting adversaries. We cover applications from Google Play and sideloaded apps.

In this role, you will develop custom-built solutions for multiple challenges and operate collaboratively across various areas. You will apply supervised learning (e.g., deep neural networks and Large Language Models) and unsupervised and semi-supervised learning (e.g., clustering and graph-based techniques). Additionally, you will support operational efficiency to ensure counterparts take action on abusive apps, and manage user-facing enforcement and telemetry to perform real-time detection, serve warnings to users, and collect telemetry data.

The US base salary range for this full-time position is $174,000-$252,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Design, build, and optimize the end-to-end machine learning systems and models that power Android's malware detection.
• Architect and implement robust data pipelines for collecting, cleaning, and processing the massive datasets required for training and fine-tuning ML models.
• Lead the development, training, and continuous improvement of our core machine learning models.
• Collaborate closely with engineers, researchers, and product managers to integrate cutting-edge ML models and LLM-powered capabilities into malware detection.
• Stay at the forefront of advancements in ML including LLMs and AI agents, advocating the adoption of new techniques to solve complex challenges in an evolving adversarial ecosystem.