Data Scientists

01 ago - Italia
Vendita al dettaglio e all'ingrosso / Import-export

Job ID: 10454637 | Amazon Development Centre Italy SRL As a Senior Applied Scientist in the Alexa AI team, you will define and drive the science roadmap for state‑of‑the‑art conversational AI systems powered by large language models, directly impacting how millions of customers interact with Alexa daily. You'll lead the design of LLM fine‑tuning, alignment, and agentic architectures that operate reliably at scale, owning end‑to‑end delivery from research formulation through production deployment.
Working at the intersection of research and production, you'll translate state‑of‑the‑art advances into customer‑facing features. Your work will span the full ML lifecycle: developing novel evaluation frameworks, building automated training pipelines, and conducting rigorous experimentation across diverse devices and endpoints.
Collaborating with engineering, product, and cross‑functional science teams across Amazon, you'll tackle the team's most complex technical challenges while maintaining practical focus on customer value.



This role offers the opportunity to publish at top‑tier conferences, generate intellectual property, and see your innovations scale to one of the world's most popular voice assistants.
Define and drive the science roadmap for conversational AI capabilities powered by large language models.
Design, implement, and evaluate novel approaches to LLM fine‑tuning, alignment (RLHF, DPO), and distillation for production deployment.
Architect agentic systems (multi‑step reasoning, tool use, planning, and orchestration) that work reliably at scale.
Develop evaluation frameworks and methodologies that go beyond standard benchmarks to capture real‑world conversational quality.
Translate research advances into customer‑facing products, working closely with engineering, product, and cross‑functional science teams.
Own end‑to‑end delivery of complex, ambiguous research initiatives from problem formulation through experimentation to production deployment, with minimal

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