Staff Software Engineer, Large Language Model and GenAI

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience with software development in C++ or Python with data structures or algorithms.
• 5 years of experience testing, and launching software products.
• 3 years of experience with software design and architecture including but not limited to leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• Experience with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning, natural language processing or other ML discipline.

Preferred qualifications:

• Master's 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 with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning, natural language processing or other ML discipline.
• Experience of applied machine learning/machine learning research.
• Experience in Android or mobile development.

In this role, you will aim to develop the machine learning system and models that power the AI personal agents. This includes tasks such as data collection design, data engineering, feature engineering, model training, and model evaluation.

The Google Pixel team focuses on designing and delivering the world's most helpful mobile experience. The team works on shaping the future of Pixel devices and services through some of the most advanced designs, techniques, products, and experiences in consumer electronics. This includes bringing together the best of Google’s artificial intelligence, software, and hardware to build global smartphones and create transformative experiences for users across the world.

• Provide technical leadership on applications development specifically for the Android and Pixel platforms.
• Decompose complex use cases into manageable technical problems and propose, design, and implement data collection solutions.
• Propose, design, and implement data collection solutions for machine learning models tuning and evaluation. 
  
  
• Design evaluation framework and carry out evaluations of the machine learning models.
• Facilitate alignment and clarify across teams to develop and maintain machine learning models.