Senior Software Engineer, AI/ML, Generative AI
Job Overview
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Job Description
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 Senior Software Engineer, AI/ML, Generative AI, 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.
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.
Responsibilities
- Write and test 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.
- Design and implement GenAI solutions, leverage ML infrastructure, and evaluate tradeoffs between different techniques and their application domains.
Minimum Qualifications
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience programming in Python or C++.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- 3 years of experience testing, maintaining, or launching software products.
- 1 year of experience with software design and architecture.
- 1 year of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
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.
Key Skills/Competency
- Python
- C++
- ML Infrastructure
- Generative AI (GenAI)
- Large Language Models (LLMs)
- Multi-Modal Models
- Large Vision Models
- Distributed Computing
- Large-Scale System Design
- Software Architecture
How to Get Hired at Google
- Research Google's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor to align your application with their innovative spirit.
- Tailor your resume: Highlight extensive experience in Python or C++, ML infrastructure, GenAI techniques (LLMs, Multi-Modal, Large Vision Models), and large-scale system design specifically for Google's engineering roles.
- Master technical interviews: Prepare rigorously for Google's demanding coding challenges, focusing on data structures, algorithms, and system design principles relevant to distributed and AI/ML systems.
- Showcase GenAI projects: Be ready to discuss specific projects where you designed and implemented GenAI solutions, explaining your role, challenges faced, and the impact of your work.
- Demonstrate leadership and versatility: Illustrate instances where you took technical leadership, collaborated effectively, debugged complex issues, and adapted to new problems across the full-stack.
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