Applied Scientist, Amazon Compliance and Safety Services
Job ID: 3207413 | Amazon Development Center (Romania) S.R.L.
Amazon's Compliance and Safety Services (CoSS) Team is looking for a smart and creative Applied Scientist to apply and extend state-of-the-art research in NLP, multi-modal modeling, domain adaptation, continuous learning and large language model to join the Applied Science team. At Amazon, we are working to be the most customer-centric company on earth. Millions of customers trust us to ensure a safe shopping experience. This is an exciting and challenging position to drive research that will shape new ML solutions for product compliance and safety around the globe in order to achieve best-in‑class, company‑wide standards around product assurance.
You will research on large amounts of tabular, textual, and product image data from product detail pages, selling partner details and customer feedback, evaluate state‑of‑the‑art algorithms and frameworks, and develop new algorithms to improve safety and compliance mechanisms. You will partner with engineers, technical program managers and product managers to design new ML solutions implemented across the entire Amazon product catalog.
Key job responsibilities
* Research and evaluate state‑of‑the‑art algorithms in NLP, multi‑modal modeling, domain adaptation, continuous learning and large language model.
* 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.
* Design and plan collection of new labels and audit mechanisms to develop better approaches that will further improve product assurance and customer trust.
* Analyze and convey results to stakeholders and contribute to the research and product roadmap.
* Collaborate with other scientists, engineers, product managers, and business teams to creatively solve problems, measure and estimate risks, and constructively critique peer research.
* Consult with engineering teams to design data and modeling pipelines which successfully interface with new and existing software.
* Publish research publications at internal and external venues.
About the team
The science team delivers custom state‑of‑the‑art algorithms for image and document understanding. The team specializes in developing machine learning solutions to advance compliance capabilities. Their research contributions span multiple domains including multi‑modal modeling, unstructured data matching, text extraction from visual documents, and anomaly detection, with findings regularly published in academic venues.
Basic Qualifications
* PhD, or a Master's degree and experience in CS, CE, ML or related field
* Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high‑performance computing
* Experience with programming languages such as Python, Java, C++
* Experience in building machine learning models for business application
* 5+ years with neural deep learning methods and machine learning
Preferred Qualifications
* Experience in professional software development
* Experience implementing algorithms using 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 statistical modeling / machine learning
Equal Employment Opportunity
Amazon is an equal opportunities employer. We are committed to building a diverse workforce. We make recruiting decisions based on your experience and skills. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information.
Posted: March 18, 2026 (Updated 1 day ago)
#J-18808-Ljbffr