Machine Learning Engineer, AGI Structured Information RetrievalJob ID: 2854800 | Amazon.com Services LLC
The AGI Structured Information Retrieval team develops the services required to query the Amazon Knowledge Graph (AKG), an LLM-optimized, foundational component in the AGI tech stack, using Natural Language.
As a Machine Learning Engineer, you'll have the power to lead the charge in developing algorithms and modeling techniques to push the boundaries of model training and deployment at scale.
Key Job ResponsibilitiesAbility to quickly learn cutting-edge technologies and algorithms in the field of ML to participate in our journey to build the best NL to Structured retrieval service.Responsible for the development and maintenance of key platforms needed for developing, evaluating and deploying ML models for real-world applications.Work with other team members to investigate design approaches, prototype new technology and evaluate technical feasibility.Work closely with Applied scientists to develop and scale machine learning models.A Day in the LifeYou will be working with a strong team of engineers and collaborating with applied scientists to develop novel processes for constructing and enhancing structured information retrieval systems; and enable high precision/recall & low latency access to knowledge in AKG.
Basic Qualifications3+ years of non-internship professional software development experience.2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience.Experience programming with at least one software programming language.Preferred Qualifications3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience.Bachelor's degree in computer science or equivalent.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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