Software Engineer, Large User Models, Core Machine Learning Full-time

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
• 2 years of experience with machine learning algorithms and tools (e.g.,TensorFlow), artificial intelligence or deep learning.
• Experience with Large Language Models, NLP or Generative AI.

Preferred qualifications:

• Master’s degree or PhD in Computer Science or related technical field.
• Experience training and improving deep learning models (e.g.,multi-task models, sequence modeling, Long Short-Term Memories (LSTMs), Convolutional Neural Networks, (CNNs), Attention.
• Experience with transformer models or recommendation systems.
• Experience with the modern ML stack or TPUs.
• Experience with applied ML and infrastructure.
• Excellent publication record in conferences (e.g., NeurIPS, RecSys).

About the job

Google Cloud’s software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. 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 software engineer, you will work on a specific project critical to Google Cloud’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. 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.

The Core ML organization is part of Google Cloud and drives ML excellence for Google. Core ML is responsible for creating a cohesive, well lit path for machine learning at Google. The organization is also responsible for developing ML infrastructure and execution around key ML efforts within Google.

The Recommendations ML team is part of Core ML. The team’s mission is to accelerate product innovations through machine learning. They deeply engage with various product areas and partner with teams to help accelerate these product innovations through applied research in recommendations and user modeling. The team generalizes successful innovations into standardized, maintainable, and production-grade solutions that can then be used by other teams and products across the Google ecosystem.

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.

The US base salary range for this full-time position is $136,000-$200,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

• Conduct applied research to improve the quality and training/serving efficiency of Large Transformer-based User Models.
• Build, improve, and productionize Large Transformer-based User Models.
• Experiment, analyze, and iterate over Large User Models for Google products (e.g., Play, Search) to improve model quality.
• Collaborate with researchers to develop new Machine Learning (ML) architectures, techniques, and metrics.
• Research in recommendation systems and Large Models from other domains (e.g., Language, Vision, Multimodal).