Minimum qualifications:

• PhD degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field and 2 years of experience in the job offered or in a Research Engineer-related occupation.
• Alternatively, will accept a Master’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field, and 5 years of experience in the job offered or in a Research Engineer-related occupation.
• Position requires 2 years of experience in the following: Python or C++; Machine learning theory application; Development with Tensorflow, JAX, or PyTorch machine learning frameworks; Leading a research agenda; Applied research from proof-of-concept to implementation

About the job

The US base salary range for this full-time position is $174,000 - $252,000 + 15% Bonus Target + equity + benefits. Transfer compensation is determined algorithmically and is non-negotiable. Learn more about how a transfer may affect your compensation package, how location changes affect compensation, and about benefits at Google at go/benefits

Position reports to the Google New York, NY office & may allow for a hybrid schedule as per Google policy.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

Responsibilities

• Apply research ideas to high-impact problems by prototyping, curating datasets, and deploying optimized machine learning systems
• Architect and implement scalable software libraries and high-quality code in Python or C++ to translate complex research into practical applications
• Drive high-stake, long-term research projects from ideation to completion by scoping project needs, managing resources, and solving ambiguous problems
• Train, evaluate, and iterate on deep neural models and reinforcement learning algorithms to continually improve agent performance and achieve research objectives
• Influence engineering best practices by championing code reviews, mentoring team members, and facilitating clear communication between research and engineering. Communicate research developments, experimental results, and project status clearly to internal teams and the broader external community