Senior Applied Scientist: ML & Gen AI for Operations

Amazon Inc.; Asti; Hybride; EUR 90 000 - 130 000; Plein temps

Résumé du poste

Amazon Web Services (AWS) is seeking a Senior Applied Scientist to deliver scalable ML and AI solutions, focusing on personalization, recommendations, and large language models. You will partner with data scientists, engineers, and product teams locally and abroad to advance AI capabilities.

You will design, implement, and deploy new AI technologies, experimenting with novel techniques and staying current with the Data Science ecosystem to drive operational improvements.

Qualifications

• 3+ years of building machine learning models for business applications.
• PhD, or Master’s degree and 6+ years of applied research experience.
• Experience programming in Java, C++, Python or related language.
• Experience with neural deep learning methods and machine learning.
• Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability.
• 4+ years of applied research experience.

Responsabilités

• Establish scalable, efficient, automated processes for large-scale data analysis and model development.
• Lead model validation and serving to customers across business domains.
• Collaborate with data engineering, tooling, operations, training, and finance to drive optimization and prediction solutions.

Connaissances

Machine learning

Programming: Java/C++/Python

Deep learning

Large-scale ML systems

Applied research

Formation

PhD or Master’s with 6+ years applied research

Outils

R

scikit-learn

Spark MLlib

MxNet

TensorFlow

NumPy

SciPy

Description du poste

Amazon Web Services (AWS) is seeking a Senior Applied Scientist to deliver scalable ML and AI solutions, focusing on personalization, recommendations, and large language models. You will partner with data scientists, engineers, and product teams locally and abroad to advance AI capabilities.

You will design, implement, and deploy new AI technologies, experimenting with novel techniques and staying current with the Data Science ecosystem to drive operational improvements.