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 varied learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Apply research ideas to high-impact real world problems through prototyping, dataset curation, model training, performance optimization, and deployment.
Develop cutting-edge techniques in Generative Media (image, video, and audio).
Optimize algorithms and models for efficient inference.
Advance capabilities in Multimodal Understanding.
Perform comprehensive model optimization to enhance performance and scale.
Minimum qualifications:
Master's degree in computer science, mathematics, applied statistics, machine learning or equivalent practical experience.
Experience of TensorFlow or ML frameworks (e.g. JAX or PyTorch).
Experience working in industry, working on projects from proof-of-concept through to implementation.
Experience with training diffusion models.
Experience conducting applied research.
Preferred qualifications:
Experience in inference optimization.
Experience with data pipelines.
Experience with training large-scale models.
Cross-functional collaboration experience.
Knowledge of machine learning, statistics, and diffusion model theories.
Master's degree in computer science, mathematics, applied statistics, machine learning or equivalent practical experience.
Experience of TensorFlow or ML frameworks (e.g. JAX or PyTorch).
Experience working in industry, working on projects from proof-of-concept through to implementation.
Experience with training diffusion models.
Experience conducting applied research.