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DESCRIPTION

Are you passionate about transforming how businesses leverage data? Do you want to work on AI technologies that redefine analytics and decision-making? Amazon is looking for a creative, motivated, and forward-thinking Applied Scientist to join our Applied AI team. In this role, you will be instrumental in shaping the future of our AI-powered analytics assistant. Your expertise will drive innovation and push the boundaries of what's possible in AI-driven insights and solutions.

We are transforming business intelligence by enabling natural language interactions with data, democratizing access to insights, and automating routine analytics tasks. Our solutions empower business users to derive deep, actionable insights without requiring technical expertise, while allowing analytics teams to focus on building robust data infrastructures and solving complex problems. From performing sophisticated data analyses to automating code generation, data pipeline creation, and business review narratives, we are reshaping how Amazon makes data-driven decisions.

We develop state-of-the-art large language models (LLMs) leveraging advanced techniques like fine-tuning, retrieval-augmented generation (RAG), and parameter-efficient tuning methods such as Low-Rank Adaptation (LoRA). Our proprietary datasets are meticulously curated to enhance model performance, ensuring robust, context-aware insights tailored to diverse business needs. We also integrate agent-based architectures to enable dynamic, autonomous interactions within complex data ecosystems, pushing the boundaries of what AI can achieve in business intelligence.

Our team actively engages with the scientific community both internally and externally. We contribute to the advancement of AI by publishing research at premier conferences such as NeurIPS, ICML, and other leading venues, driving innovation and fostering knowledge exchange.


Key job responsibilities
- Design, develop, and deploy state-of-the-art machine learning models that power natural language understanding and data analytics capabilities.
- Push the boundaries of ML and Generative AI techniques to deliver personalized, context-aware insights at scale.
- Collaborate closely with software engineers and product managers to integrate machine learning models into platform production systems, ensuring scalability and efficiency.
- Conduct pioneering research in NLP, multimodal systems, and machine learning to enhance capabilities.
- Analyze vast datasets to uncover actionable insights and continuously refine models’ performance through rigorous experimentation.
- Drive innovation by contributing to the scientific community through research publications at conferences such as ICML, NeurIPS, and Amazon-hosted events.

A day in the life
- Early mornings to complete check-in, project updates, and code reviews
- Team stand-up meeting with cross-functional groups for updates and alignment
- Focused research time: reading papers, brainstorming, and experiment design
- Afternoons dedicated to model development, coding, and experimentation
- Engineering collaboration on deployment and optimization
- Results analysis and documentation
- End-of-day team communication and planning