Research Engineer, Machine Learning Theory (google)

Qualifications

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.
  

Summary

• 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.
  
  View Orignal JOB on: italents.net
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
  

Description

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.