Selection Monitoring team is responsible for making the biggest catalog on the planet even bigger. In order to drive expansion of the Amazon catalog, we develop advanced ML/AI technologies to process billions of products and algorithmically find products not already sold on Amazon. We work with structured, semi-structured and Visually Rich Documents using deep learning, NLP and image processing.
The role demands a high-performing and flexible candidate who can take responsibility for success of the system and drive solutions from research, prototype, design, coding and deployment. We are looking for Applied Scientists to tackle challenging problems in the areas of Information Extraction, efficient crawling at internet scale, developing ML models for website comprehension and agents to take multi-step decisions. You should have depth and breadth of knowledge in text mining, information extraction from Visually Rich Documents, semi structured data (HTML) and advanced machine learning and reinforcement learning methods. You should also have programming and design skills to manipulate semi-structured and unstructured data and systems that work at internet scale.
You will encounter many challenges, including:
Scale (build models to handle billions of pages),
Accuracy (requirements for precision and recall)
Speed (generate predictions for millions of new or changed pages with low latency)
Diversity (models need to work across different languages, market places and data sources)
You will help us to:
Build a scalable system which can algorithmically extract information from world wide web.
Intelligently cluster web pages, segment and classify regions, extract relevant information and structure the data available on semi-structured web.
Build systems that will use existing Knowledge Bases to perform open information extraction at scale from visually rich documents.
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Using AI, NLP and advances in LLMs/SLMs and agentic systems to create scalable solutions for business problems.
Developing models for efficiently crawling web, automate extraction of relevant information from large amounts of Visually Rich Documents and optimize key processes.
Designing, developing, evaluating and deploying, innovative and highly scalable ML models, esp. leveraging latest advances in RL-based fine-tuning methods like DPO, GRPO etc.
Identifying latest technical/research trends applicable for the problems of efficient web navigation and web-scale information extraction and adapting them to concrete open problems.
Influencing software engineering teams to drive and optimize model implementations.
Challenging status quo in the current end-to-end production stack and ML models and identifying opportunities for simplification, improvements, cost-saving and innovation.
Establishing scalable, efficient, automated processes for large scale model development, model validation and model maintenance.
Leading projects and mentoring other scientists, interns, engineers in the use of ML techniques.
Publishing innovation in research forums.
Basic qualifications
6+ years of building machine learning models for business application experience
PhD, or Master's degree
Experience programming in Java, C++, Python or related language
Experience with neural deep learning methods and machine learning
Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Preferred qualifications
Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Experience with large scale distributed systems such as Hadoop, Spark etc.
- Experience using deploying large-scale ML models.
- Experience in advanced LLM training/fine-tuning methods.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. 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. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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