Applied Scientist

About the Role

AWS Applied AI Solutions (AAIS) is building AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that make complex things easy and hard things possible.

Key Responsibilities

• Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries.
• Drive end-to-end Machine Learning and GenAI projects with ambiguity, scale, and complexity.
• Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy models into production.
• Work with scientists and software engineers to deliver machine-learning and science solutions to production.
• Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation, and serving.
• Act as the technical practitioner in AI/ML; lead efforts within the team and across other teams; research new and innovative science approaches.
• Communicate scientific methodology clearly to audiences from diverse backgrounds.

Requirements & Tech Stack

• 3+ years of building models for business application experience.
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
• Experience programming in Java, C++, Python or related language.
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.
• Experience using Unix/Linux.
• Experience in professional software development.
• Experience building large-scale machine learning models and infrastructure for online recommendation, ads ranking, personalization, or search.
• Experience with AI, Big Data, large scale distributed systems, online recommendation, personalization, or search.

Qualifications

• Amazon values diverse experiences. We encourage candidates to apply even if they do not meet all preferred qualifications.
• If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, we encourage you to apply.