Research Engineer - Generative AI & Diffusion Models

WeAreTechWomen; Greater London; Hybrid; GBP 120,000 - 170,000; Full time

Job summary

Google DeepMind is seeking a research-focused Software Engineer embedded across the company to design, prototype, and deploy AI-driven systems at scale. You will work on large-scale experiments and collaborate with universities and institutes globally to accelerate AI innovation.

Responsibilities include prototyping, dataset curation, model training, and performance optimization across generative media and multimodal understanding, with a strong emphasis on safe, ethical AI development.

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.

Responsibilities

• 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.

Skills

TensorFlow / ML frameworks

Industry experience

Training diffusion models

Applied research

Diffusion model theories

Education

Master's degree in CS / math / stats

Tools

JAX

PyTorch

Job description

Google DeepMind is seeking a research-focused Software Engineer embedded across the company to design, prototype, and deploy AI-driven systems at scale. You will work on large-scale experiments and collaborate with universities and institutes globally to accelerate AI innovation.

Responsibilities include prototyping, dataset curation, model training, and performance optimization across generative media and multimodal understanding, with a strong emphasis on safe, ethical AI development.