About the position
The Discovery Tech team helps customers discover and engage with new, popular and relevant products across Amazon worldwide. We do this by combining technology, science, and innovation to build new customer-facing features and experiences alongside agentic tools for marketers. You will be responsible for creating and building critical services that automatically generate, target, and optimize Amazon's cross-category marketing and merchandising. Through the enablement of intelligent marketing campaigns that leverage machine-learning models, you will help to deliver the best possible shopping experience for Amazon's customers all over the globe. We are looking for analytical problem solvers who enjoy diving into data, excited about data science and statistics, can multi-task, and can credibly interface between engineering teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your domain spans the design, development, testing, and deployment of data-driven and highly scalable machine learning solutions in product recommendation. As an Applied Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.
Requirements
- PhD in Computer Science, Machine Learning, AI, or related field; OR Master's degree with 2+ years of relevant industry experience
- Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow, etc.)
- Experience building and deploying machine learning models in production environments
- Proficiency in natural language processing techniques and applications
- Strong publication record or demonstrated practical experience in machine learning
Nice-to-haves
- Experience working with large language models (LLMs) and prompt engineering
- Knowledge of multi-agent systems and their applications
- Background in recommendation systems or personalization technologies
- Experience with uncertainty quantification in AI systems
- Ability to design and implement automated testing frameworks for ML systems
- Previous experience in AI labs or AI-focused startups
- Strong communication skills and ability to translate complex technical concepts to diverse audiences
- Experience with model evaluation and validation methodologies
- Track record of innovation in applied machine learning
Benefits
- Equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package
- Full range of medical, financial, and/or other benefits
