Research Engineer

Google Google · Big Tech · Mountain View, CA +1

Research Engineer at Google DeepMind focused on applying advanced ML models and research to solve real-world problems. Responsibilities include rapid prototyping, experimental design, developing scalable code, training and evaluating ML models and agents, and collaborating with research scientists and engineers. Requires a Master's degree and 2 years of experience in ML algorithm design, statistical analysis, Python/C++, and ML frameworks like TensorFlow or JAX.

What you'd actually do

  1. Apply advanced machine learning models and research to solve high-impact, real-world problems through rapid prototyping and experimental design
  2. Develop high-quality, scalable code in Python or C++ by translating complex research concepts into robust algorithms and software libraries
  3. Train, evaluate, and iteratively improve the performance of machine learning models and agents throughout the entire research and development cycle
  4. Plan and execute multi-week projects, independently solving technical challenges and ensuring your work aligns with broader team goals
  5. Collaborate closely with research scientists and engineers, clearly communicating project plans, developments, and experimental results to diverse audiences. Provide software design expertise to research projects, writing effective design documents and contributing to the enhancement of team tools and processes

Skills

Required

  • Master's degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field
  • 2 years of experience in the job offered or in a Research Engineer-related occupation
  • Algorithm design for machine learning applications
  • Statistical analysis for machine learning
  • Python or C++
  • TensorFlow or JAX
  • Software engineering for large-scale ML projects

Nice to have

  • Translate complex research concepts into robust algorithms and software libraries
  • Train, evaluate, and iteratively improve the performance of machine learning models and agents
  • Plan and execute multi-week projects
  • Independently solving technical challenges
  • Collaborate closely with research scientists and engineers
  • Clearly communicating project plans, developments, and experimental results to diverse audiences
  • Provide software design expertise to research projects
  • Writing effective design documents
  • Contributing to the enhancement of team tools and processes

What the JD emphasized

  • advanced machine learning models and research
  • rapid prototyping and experimental design
  • high-quality, scalable code
  • robust algorithms and software libraries
  • train, evaluate, and iteratively improve the performance of machine learning models and agents
  • research and development cycle
  • technical challenges
  • research scientists and engineers
  • software design expertise
  • Algorithm design for machine learning applications
  • Statistical analysis for machine learning
  • large-scale ML projects

Other signals

  • apply advanced machine learning models and research
  • rapid prototyping and experimental design
  • translate complex research concepts into robust algorithms and software libraries
  • train, evaluate, and iteratively improve the performance of machine learning models and agents
  • collaborate closely with research scientists and engineers