About
Google DeepMindGoogle DeepMind · Mountain View/San Francisco
Research Engineer, AI for Weather and Energy
26.9.2025
Description
- Design, implement, scale and evaluate state-of-the-art deep learning models (e.g., Transformers, GNNs) and software prototypes for sustainability-related problems.
- Build robust and scalable data processing and training pipelines to enable rapid research iterations.
- Report and present findings and developments clearly and efficiently, both internally and externally.
- Contribute to team collaborations to meet ambitious research and product goals.
- Engage with application and product needs, to inform research and engineering decisions.
Qualifications
- BSc, MSc or PhD/DPhil degree in computer science, mathematics, applied stats, machine learning or equivalent practical experience.
- Strong background in deep learning, with proven experience with relevant architectures (e.g., Transformers, GNNs, etc).
- Strong background in machine learning foundations (esp. supervised learning, probabilistic modeling, graph-based learning, and/or optimization).
- Excellent software engineering skills with a proven ability to build robust and scalable systems.
- Proficiency in deep learning frameworks like JAX, TensorFlow, or PyTorch is essential.
- Experience with either large-scale data processing frameworks (e.g., Apache Beam, Spark) or distributed training infrastructure.
- Core contributor to OSS projects.
- Experience with Python and its ecosystem.
- Experience with remote sensing data.
- Experience translating research innovations into product applications.
- Commitment/experience/expertise in weather/environmental sustainability and/or allied areas (e.g., fluid dynamics, complex systems, combinatorial optimization, remote sensing).
- A proven track record of publications in top-tier conferences and/or journals.
Benefits
The US base salary range for this full-time position is between $182,000 - $215,000 + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.