Applied Scientist, Amazon Compliance and Safety Services

Amazon Amazon · Big Tech · Bucharest, Romania · Administrative Support

Research Scientist role focused on applying and extending state-of-the-art research in NLP, multi-modal modeling, domain adaptation, continuous learning, and large language models to improve product compliance and safety at Amazon. The role involves researching and evaluating algorithms, designing new algorithms for business impact (e.g., synthetic data generation, active learning, grounding LLMs), and collaborating with engineering and product teams to implement ML solutions across the product catalog. The team specializes in image and document understanding for compliance capabilities, with a focus on publishing research.

What you'd actually do

  1. Research and Evaluate state-of-the-art algorithms in NLP, multi-modal modeling, domain adaptation, continuous learning and large language model.
  2. Design new algorithms that improve on the state-of-the-art to drive business impact, such as synthetic data generation, active learning, grounding LLMs for business use cases
  3. Design and plan collection of new labels and audit mechanisms to develop better approaches that will further improve product assurance and customer trust.
  4. Analyze and convey results to stakeholders and contribute to the research and product roadmap.
  5. Collaborate with other scientists, engineers, product managers, and business teams to creatively solve problems, measure and estimate risks, and constructively critique peer research

Skills

Required

  • Experience building machine learning models or developing algorithms for business application
  • Experience programming in Java, C++, Python or related language
  • Currently has, or is in the process of obtaining, a Bachelor's degree or above in Engineering, Computer Science, Machine Learning, Statistics, Physics, or related fields

Nice to have

  • Experience in professional software development
  • Experience implementing algorithms using both toolkits and self-developed code
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience working cross functionally across several teams
  • Experience with modeling tools such as scikit-learn, PyTorch, numpy, scipy etc.

What the JD emphasized

  • state-of-the-art research
  • state-of-the-art algorithms
  • state-of-the-art

Other signals

  • NLP
  • multi-modal modeling
  • domain adaptation
  • continuous learning
  • large language model
  • synthetic data generation
  • active learning
  • grounding LLMs