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X Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 2 years of experience with software development in the Python programming language.
• 1 year of experience with Generative AI.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
• 2 years of experience with data structures and algorithms.
• Experience with GoLang programming language.

About the job

SecLM is an Artificial Intelligence (AI) platform of Gemini that offers added value for security partners and customers building security applications. The platform applies Retrieval-augmented generation (RAG), agentic workflows and grounding, with evaluations incorporating the unsurpassed security intelligence such as Google's visibility into the threat landscape and Mandiant's frontline intelligence on vulnerabilities, malware, threat indicators, and behavioral threat actor profiles.Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) 15% bonus target equity benefits

Learn more about benefits at Google .

Responsibilities

• Write 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.
• Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.