Research Software Engineer
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- Experience with Large Language Models.
- Experience with Natural Language Processing.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
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 handle information at 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 AI and Research Accelerator, a specialized team within the newly formed AI Innovation and Research organization, you will translate the latest breakthroughs from DeepMind into AI/ML experiences for Google's highest-priority Platforms and Devices (P&D) initiatives.
In this role, you will work alongside a team of ML specialists dedicated to highly impactful projects with the potential to reach billions of users, architecting solutions and directly guiding product teams from concept to launch. You will gain unparalleled visibility, driving deeply impactful work that frequently takes center stage at major events like Google I/O and Made by Google, while helping lead the next-generation of AI-driven innovation.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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Oversee the end-to-end technical architecture and drive technical consensus across teams.
- Design high-level systems and build scalable, reusable ML infrastructure that serves as a foundation for multiple product teams.
- Establish the standard for engineering excellence by steering architectural reviews, mentoring engineers on system design, and identifying strategic opportunities to accelerate innovation across the entire Platforms and Devices ecosystem.
- Cultivate deep partnerships with product teams and researchers at DeepMind, serving as the primary technical voice and engineering liaison for the team, while translating state-of-the-art AI research into tangible user experiences in products by guiding engineers through integrations, resolving technical ambiguities, and aligning cross-functional stakeholders on a unified technical goal.
- Lead the end-to-end development of AI features by orchestrating the team's engineering efforts under tight, high-visibility deadlines.
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