Software Engineering Manager, Machine Learning, Pixel
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience with software development Python or C++
- 5 years of industry experience with any of the Machine Learning (ML) fields including but not limited to speech/audio, reinforcement learning, and ML infrastructure.
- 5 years of industry experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 3 years of experience in a technical leadership role with 2 years of experience in a people management, supervision/team leadership role.
Preferred qualifications:
- Master Degree or PhD in Machine Learning, or in a related technical field.
- Experience with Natural Language Processing (NLP), Large Language Model (LLM), and Computer Vision (CV).
- Experience in Android or mobile development.
About the job
Google is building Generative Artificial Intelligence (GenAI) features on Pixel that are personal, private and on device. Google believes technology has emerged Large Language Models (LLM) to enable such an agent and create a differentiated and transformative experience, especially with Pixel's unique take on hardware (e.g. Tensor chip) + software + Artificial Intelligence (AI).
This project will address many cutting-edge problems, such as: LLM, AI agent interaction with tools/Application programming interfaces (API), private and personalized AI, etc.
In this role, you will be responsible for developing the Machine Learning (ML) system and models that power the Pixel personal agent. This includes tasks such as data collection design, data engineering, feature engineering, model training, and model evaluation. You will work with engineers, researchers and product managers across Google to design and implement new features for the personal agent.
Responsibilities
- Design Machine Learning (ML) systems for new GenAI features on Pixel phones.
- Develop and tune ML models.
- Propose, design and implement data collection solutions.
- Design evaluation framework and carry out evaluations of the ML models.
- Stay up-to-date on the latest ML techniques and technologies and apply them to Google's technical solutions.
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