Research Engineer, Gemini Omni, DeepMind

• 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, including 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

Technologies:

• AI
• Embedded
• Hardware
• Machine Learning
• Mobile
• Model Training
• PyTorch
• TensorFlow

More:

At Google, our research-focused Software Engineers are embedded throughout the company, where they can set up large-scale tests and quickly deploy promising ideas. Ideas may come from internal projects and collaborations with research programs at partner universities and technical institutes around the world. Our engineers create experiments and prototypes, design new architectures, and address real-world problems across areas such as artificial intelligence, data mining, natural language processing, hardware and software performance analysis, mobile compiler improvements, and core search. They also stay connected to the research community through university partnerships and published papers. At Google DeepMind, we are a pioneering AI lab with interdisciplinary teams advancing AI to address complex global challenges and accelerate product innovation for billions of users. We work toward widespread public benefit and scientific discovery, with safety and ethics as our highest priorities. Our global teams offer varied learning opportunities and career pathways for people driven to achieve exceptional results through collective effort.

• Masters degree in computer science, mathematics, applied statistics, or machine learning, or equivalent practical experience
• Experience with TensorFlow or machine learning frameworks such as JAX or PyTorch
• Experience working in industry on projects from proof of concept through implementation
• Experience training diffusion models
• Experience conducting applied research
• Experience in inference optimization
• Experience with data pipelines
• Experience training large-scale models
• Cross-functional collaboration experience
• Knowledge of machine learning, statistics, and diffusion model theories