Senior Software Engineer, AI/ML GenAI, Google Cloud
In most instances, this position requires in-person interviews as part of the hiring process.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; New York, NY, USA.Minimum qualifications:
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Responsibilities
Learn more about benefits at Google .
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; New York, NY, USA.Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience programming in Python or C++.
- 3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, 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 ).
- Master's degree or PhD in Computer Science or related technical field.
- 5 years of experience with data structures and algorithms.
- 1 year of experience in a technical leadership role.
- Experience developing accessible technologies.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Responsibilities
Learn more about benefits at Google .
- 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.
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