Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages (e.g., Java).
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with large-scale application design and architecture.
- 3 years of experience with Android application development.
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical fields.
- Experience with Android mobile development.
- Experience with developer-facing API design.
- Experience with ML-powered feature development and productization.
- Strong ability to partner with research and platform teams to drive mobile AI innovation.
- Skill in building scalable system services that bridge high-level application APIs with low-level hardware acceleration.
About the jobThis role is ideal for those who are enthusiastic about delivering groundbreaking mobile experience changes by leveraging AI technology within the Android ecosystem.
As a Senior Software Engineer on the AI Core Solutions team, you are responsible for the end-to-end delivery and optimization of on-device GenAI capabilities for first-party and third-party developers.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Collaborate closely with DeepMind, CoreML, and Research to adapt and implement the Gemini Nano model for mobile user applications.
- Build simple, solid, and impactful developer-facing ML Kit and AICore APIs to empower both first-party and third-party app developers to create innovative user experiences with GenAI technologies in Android applications.
- Optimize the on-device inference latency and resource consumption for various Gemini Nano versions (V1, V2, V3, V4, etc.), ensuring they are optimally integrated into the mobile environment across the Android ecosystem.
- Work closely with product teams to innovate and implement novel user experiences and agentic workflows, leveraging techniques like Functional Calling, Retrieval-Augmented Generation (RAG), constraint decoding, gemma embedding, multi-modal processing, traditional machine learning models, LoRAs, and targeted Large Language Model (LLM) fine-tuning.
