Research Engineer, Gen Media, DeepMind

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.

Responsibilities

• Lead ambitious team collaborations, aligning efforts across DeepMind to execute on shared, high-impact research goals.
• Report and present complex research breakthroughs clearly and efficiently. Act as a principal representative of our work internally and externally at global venues.
• Build, lead, and maintain strategic collaborations with key external research labs and leading individuals in the field.

Minimum qualifications:

• Bachelor's degree in Computer Science, Mathematics, Applied Stats, Machine Learning or or equivalent practical experience.
• 5 years of experience with TensorFlow or similar ML frameworks (e.g., JAX or PyTorch).
• 2 years of experience in an applied research setting.

Preferred qualifications:

• Experience with training diffusion models.
• Experience working in industry and on projects from proof-of-concept through to implementation.