Applied Scientist Gen AI - Amazon Advertising, Creativex

Amazon Amazon · Big Tech · London, United Kingdom · Applied Science

Applied Scientist role focused on developing novel multi-modal generative AI agentic architectures and models for advertising creatives, integrating and deploying ML projects, curating datasets, and performing analysis. The role involves research, publication, and collaboration with cross-functional teams.

What you'd actually do

  1. Drive the invention and development of novel multi-modal agentic architectures and models for the use of Generative AI methods in advertising.
  2. Work closely and integrate end-to-end proof-of-concept Machine Learning projects that have a high degree of ambiguity, scale and complexity.
  3. Build interface-oriented systems that use Machine Learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models.
  4. Curate relevant multi-modal datasets.
  5. Perform hands-on analysis and modeling of experiments with human-in-the-loop that eg increase traffic monetization and merchandise sales, without compromising the shopper experience.

Skills

Required

  • Experience researching, publishing and or developing multi-modal Generative AI systems.
  • PhD, or Master's degree and several years of experience of CS, CE, ML or related field experience
  • Experience with data curation and creation.
  • Programming experience in Java, C++, Python or related ML language.

Nice to have

  • Advanced degree in Computer Science, Mathematics, Statistics, Economics, or related quantitative field.
  • Published relevant research work in academic conferences (e.g. CVPR, ICCV, ECCV, NEURIPS, etc) or industry circles.
  • Effective verbal and written communication skills with non-technical and technical audiences.
  • Experience working with large real-world data sets and designing scalable models from big data.
  • Thinks strategically, but stays on top of tactical execution.
  • Exhibits excellent business judgment; balances business, product, and technology very well.
  • Familiarity with computational advertising.

What the JD emphasized

  • novel multi-modal agentic architectures and models
  • multi-modal Generative AI systems
  • multi-modal paradigms, models, datasets and agentic architectures
  • multi-modal Generative AI methods

Other signals

  • multi-modal generative AI
  • LLMs
  • generative audio
  • computer vision
  • latent diffusion models
  • agentic architectures