Senior Software Engineer, Machine Learning and Embedded Systems
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This role may also be located in our Playa Vista, CA campus.
Applicants in the County of Los Angeles: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Los Angeles, CA, USA; San Francisco, CA, USA.Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in C++ and Python programming languages, and with data structures/algorithms.
- 3 years of experience testing, maintaining or launching software products, and 1 year of experience with software design and architecture.
- 2 years of experience with Machine Learning.
Preferred qualifications:
- Master's degree or PhD in Computer Science or a related technical field.
- Experience with perception and object detection.
- Experience in algorithmic optimization for embedded systems.
- Experience with profiling, benchmarking and presentation of complex data.
- Knowledge of ML frameworks.
- Familiarity with Android development.
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.
In this role, you will play a crucial part in ensuring machine learning algorithms run efficiently across multiple hardware platforms. You will need to have knowledge of Machine Learning engineering, working on next generation hardware, embedded systems, and a desire to push the boundaries of performance optimization. You will have strong teamwork and collaborative skills, as this is a highly collaborative role that will involve daily communication and coordination with our partners.
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.
Responsibilities
- Integrate Machine Learning (ML) frameworks on android and embedded systems.
- Work closely with other ML practitioners, software engineers, and hardware teams to optimize performance across different platforms.
- Develop an efficient ML runtime system and optimized ML operator libraries for different hardware options.
- Analyze the performance of machine learning models, identifying bottlenecks in the software stack or hardware utilization.
- Optimize machine learning models, applying techniques like model quantization, model pruning, Neural Architecture Search (NAS), and hardware optimization to improve runtime performance.
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Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy, Know your rights: workplace discrimination is illegal, Belonging at Google, and How we hire.
If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.