The role
From Google's own posting.
At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
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.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google .
Ingest and curate real-world grid operator data.
Formulate AC/DC Optimal Power Flow (AC-OPF), economic dispatch, and congestion management constraints (thermal limits, voltage stability, power flow equations) for ML consumption.
Serve as primary technical liaison with national laboratories (e.g., LLNL), utilities, grid operators, and internal teams (e.g., Google E&P, Tapestry, Google Cloud).
Minimum qualifications:
10 years of experience in Power Systems, Energy, or Power Plant Engineering.
2 years of experience in leadership or people management with a focus on hiring, mentoring, and retaining talent.
Experience working with real grid operator data.
Experience building power systems optimization models in Python or Julia.
Experience in software engineering.
Preferred qualifications:
Experience with machine learning frameworks and evaluating ML models, including data pipelines, training, and deployment.
Excellent project management, verbal communication, analytical and written skills.
10 years of experience in Power Systems, Energy, or Power Plant Engineering.
2 years of experience in leadership or people management with a focus on hiring, mentoring, and retaining talent.
Experience working with real grid operator data.
Experience building power systems optimization models in Python or Julia.
Experience in software engineering.