About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to manage information at a massive scale, and extend well beyond web search. 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’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. 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.

As a part of the Gboard team, the Android Input Research (AIR) Team conducts product-oriented research and development in the space of human-computer interactions by using technologies including on-device ML models, large language models (LLMs), and GenAI agents, with the goal of enabling delightful and useful multi-modal text-entry user experiences on mobile devices.The Platforms and Devices team encompasses Google's various computing software platforms across environments (desktop, mobile, applications), as well as our first-party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world.

The US base salary range for this full-time position is $174,000-$252,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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

• Design methods to compile, synthesize, and organize natural-language datasets that form the foundation of training and evaluating ML models.

• Build and iteratively refine high-performance training and evaluation pipelines for a set of ML models ranging in size from small, fast on-device classifiers to powerful on-server Large Language Models (LLMs).

• Deploy and integrate AI models in the Gboard app to support early prototyping and product feature launches.

• Stay abreast with latest technologies in the area of LLMs and agentic AI and judiciously integrate them with prototyping and development efforts.

• Partner with UX/HCI researchers to create experimental protocols for collecting high-quality online and offline user data to further augment the datasets for ML model training and evaluation.

Minimum qualifications

• Bachelor’s degree or equivalent practical experience.

• 5 years of experience with software development in one or more programming languages.

• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.

• 3 years of experience with one or more of the following: Natural Language Processing or Large Language Models.

Preferred qualifications

• Master's degree or PhD in Computer Science or related technical field.

• 5 years of experience with data structures and algorithms.

• Experience in natural language processing (NLP) and machine learning (ML), with developing and evaluating NLP/ML models.

• Experience with training and evaluation pipelines for various model sizes, most importantly LLMs.

• Familiarity with data collection methodologies, including user studies.

• Ability to compile and organize large natural-language datasets for ML.