Expert Machine Learning Engineer

hace 1 mes


Barcelona, Barcelona, España Amazon A tiempo completo

About Amazon's Transportation Systems


Amazon's transportation network is one of the fastest-growing and most complex in the world. As a machine learning scientist, you will have the opportunity to build state-of-the-art systems that make business decisions impacting billions of dollars annually.


We are looking for a talented individual with a strong academic background in machine learning, time series forecasting, and/or optimization. You will focus on developing and researching machine learning solutions and algorithms for core planning systems within Amazon Transportation Services.


The team works backwards from Amazon's customers, aiming to make transportation faster, cheaper, safer, more reliable, and ecologically sustainable. We own scalable algorithms, models, and systems that improve customer experience in middle-mile.


Key Responsibilities:



  • Develop and research machine learning solutions and algorithms for core planning systems
  • Work on improving customer experience in middle-mile
  • Collaborate with cross-functional teams to integrate machine learning models into production environments
  • Stay up-to-date with the latest advancements in machine learning and apply them to real-world problems

Requirements:



  • PhD or Master's degree in Computer Science, Electrical Engineering, Machine Learning, or related field
  • Experience in building models for business applications
  • Experience programming in Java, C++, Python, or related languages
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Strong understanding of machine learning concepts and techniques
  • Excellent communication and collaboration skills

Benefits:


We offer a competitive salary of €120,000 - €180,000 per year, depending on experience, as well as excellent benefits and opportunities for growth and development. Amazon is an equal opportunities employer and welcomes applications from diverse candidates.



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