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Job Details
Posted date: Dec 09, 2025
Location: Seattle, WA
Level: Senior
Estimated salary: $205,000
Range: $166,000 - $244,000
Description
Be responsible for end-to-end ownership of machine learning models and associated data sets, from design, to prototype, training, evaluation, and optimization. Manage on-device deployment and optimization including ML runtimes (software and hardware), memory access patterns, bandwidth requirements, and power modeling. Be responsible for developing tools and infrastructure used to train and evaluate audio ML models, and to document progress, results, and processes. Manage low-level on-device integration, including auxiliary components for audio pre- and post-processing and feature generation.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.
The Google Augmented Reality team is a group of experts tasked with
building the foundations for great immersive computing and building
helpful, delightful user experiences. We're focused on making immersive computing accessible to billions of people through mobile devices, and our scope continues to grow and evolve.
The US base salary range for this full-time position is $166,000-$244,000 + 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.
Qualifications
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: 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. 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications: Master's degree or PhD in Computer Science or a related technical field. 2 years of experience working with audio or DSP algorithms. 1 year of experience in a technical leadership role.
Extended 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: 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. 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
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