Senior Machine Learning Engineer for Population Dynamics Forecasting
hace 2 semanas
Company Overview
Amazon, a global leader in e-commerce and cloud computing, is seeking an exceptional Senior Machine Learning Engineer to join its PXT Finance team. This team is responsible for developing cutting-edge machine learning models to predict population dynamics and optimize expense planning.
Estimated Salary: $250,000 - $350,000 per year
About the Role
As a Senior Machine Learning Engineer, you will be part of a small team of scientists dedicated to forecasting and simulation applications for expense and headcount planning. You will work closely with cross-functional teams, including data scientists, software engineers, and product managers, to integrate forecasting solutions into Amazon's cost planning processes.
Key Responsibilities
- Lead the development of distributed simulations for population dynamics forecasting and expense planning
- Implement state-of-the-art graph deep learning models for tasks such as node survival analysis and spatio-temporal time series forecasting
- Collaborate with cross-functional teams to integrate forecasting solutions into Amazon's cost planning processes
- Leverage big data processing frameworks such as Apache Spark to ingest high volume headcount data into machine learning workflows
- Apply GPU programming frameworks such as PyTorch and CUDA to deploy fast and scalable planning solutions
Requirements
- PhD or Master's degree in Computer Science, Engineering, or related field
- 3+ years of experience building models for business applications
- Experience programming in Java, C++, Python, or related languages
- Strong background in algorithms, data structures, numerical optimization, and parallel computing
About the Team
Our team is committed to delivering efficient and innovative solutions by constantly developing and improving our research and engineering skills. We strive to keep pace with the advancements in machine learning technology and rapidly scale our productivity and impact.
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