Research Engineer, Machine Learning Theory at Google
New York, NY 10001
About the Job
Minimum qualifications:Bachelor’s degree or equivalent practical experience.5 years of experience with software development in one or more programming languages, and with data structures/algorithms.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, data processing, debugging).Preferred qualifications:PhD or industry experience in applied machine learning or related research.Experience working with large datasets and distributed computing.Understanding of theoretical machine learning principles.Excellent communication skills and ability to work in a collaborative research environment
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.Google Research is seeking an ML Theory Research Engineer to bridge the gap between theoretical research and practical applications
You will work with Machine Learning (ML) Theory researchers to implement, test, and scale new machine learning algorithms
You will apply your engineering expertise to translate theoretical models into functional systems, optimizing performance and scalability for real-world deployment
You will have the opportunity to work alongside world-renowned experts, publish, and contribute to Google products.The US base salary range for this full-time position is $161,000-$239,000 + 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 Implement, test, and optimize machine learning algorithms based on theoretical research.Collaborate with researchers to develop and validate new models, ensuring they are efficient and scalable.Design experiments to evaluate algorithm performance on large-scale datasets.Work with product teams to integrate theoretical insights into production systems.Contribute to research publications and open-source initiatives.
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.Google Research is seeking an ML Theory Research Engineer to bridge the gap between theoretical research and practical applications
You will work with Machine Learning (ML) Theory researchers to implement, test, and scale new machine learning algorithms
You will apply your engineering expertise to translate theoretical models into functional systems, optimizing performance and scalability for real-world deployment
You will have the opportunity to work alongside world-renowned experts, publish, and contribute to Google products.The US base salary range for this full-time position is $161,000-$239,000 + 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 Implement, test, and optimize machine learning algorithms based on theoretical research.Collaborate with researchers to develop and validate new models, ensuring they are efficient and scalable.Design experiments to evaluate algorithm performance on large-scale datasets.Work with product teams to integrate theoretical insights into production systems.Contribute to research publications and open-source initiatives.
