Senior Research Engineer

Google Google · Big Tech · New York, NY +1

Google DeepMind is seeking a Senior Research Engineer to apply AI research to high-impact problems, prototype, curate datasets, and deploy optimized ML systems. The role involves architecting and implementing scalable software libraries, driving long-term research projects, and training/evaluating deep neural models and RL algorithms. Requires a PhD or Master's degree with significant experience in ML theory, frameworks like Tensorflow/JAX/PyTorch, and leading research agendas.

What you'd actually do

  1. Apply research ideas to high-impact problems by prototyping, curating datasets, and deploying optimized machine learning systems
  2. Architect and implement scalable software libraries and high-quality code in Python or C++ to translate complex research into practical applications
  3. Drive high-stake, long-term research projects from ideation to completion by scoping project needs, managing resources, and solving ambiguous problems
  4. Train, evaluate, and iterate on deep neural models and reinforcement learning algorithms to continually improve agent performance and achieve research objectives
  5. 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

Skills

Required

  • 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
  • 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
  • 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

Nice to have

  • 15% bonus target
  • equity
  • benefits determined by role, level, and location
  • hybrid schedule

What the JD emphasized

  • Apply research ideas to high-impact problems
  • deploying optimized machine learning systems
  • translate complex research into practical applications
  • long-term research projects
  • Train, evaluate, and iterate on deep neural models and reinforcement learning algorithms

Other signals

  • advancing AI development
  • pushing the boundaries
  • apply research ideas to high-impact problems
  • deploying optimized machine learning systems
  • translate complex research into practical applications
  • long-term research projects from ideation to completion
  • Train, evaluate, and iterate on deep neural models and reinforcement learning algorithms