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 various learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Responsibilities
- Determine how to run research methods with large-scale compute.
- Optimize performance through engineering and benchmarking.
- Solve key research challenges by designing and running experiments, sharing analyses and proposing next steps.
- Integrate engineering expertise into research projects, sharing skills and knowledge with other engineers and researchers.
- Design, build, and improve infrastructure for research.
Qualifications
Minimum qualifications:
- Bachelor's degree in computer science, electrical engineering, science, mathematics or equivalent experience.
- 2 years of experience in programming, specifically with Python-based scientific libraries such as JAX, PyTorch, TensorFlow, NumPy.
- Experience in developing or implementing machine learning models in a production or research environment.
Preferred qualifications:
- Experience with large-scale system design, distributed systems.
- Experience in data engineering and visualisation.
- C++ or broader programming experience.
- Academic research experience in machine learning, publications, or research experience in related fields.
- Knowledge of distributed computation for ML, especially in the context of accelerators (e.g. sharding, multi-host computation).
- Strong communication skills (via discussion, presentation, technical and research writing, whiteboarding).