Applied Scientist: ML & Physics-Informed Modeling

Amazon

Massachusetts

On-site

USD 136,000 - 184,000

Full time

26 hours ago
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Benefits offered by this job

Health insurance
401(k) matching
Paid time off
Parental leave

Job summary

Amazon's Science Software team is seeking an Applied Scientist to blend ML, applied mathematics, and production software across computer vision, optimization, and orchestration. You will frame ambiguous business problems as tractable scientific ones and build models, prototypes, and real-time systems.

You will collaborate with multiple science and engineering teams to deploy solutions in a scalable architecture, working on edge devices and on-premise platforms within a fast-paced fulfillment

Qualifications

  • Advanced degree in engineering, technology, computer science, or math (or in progress).
  • Experience with Python, Java, and C++.
  • Strong foundation in applied mathematics, statistics, and ML.
  • Experience with PyTorch, TensorFlow, and SciPy stack.

Responsibilities

  • Own R&D of ML and scientific solutions spanning ML, modeling, and production deployment.
  • Prototype end-to-end solutions for new problem domains.
  • Prototype and evaluate hardware and edge models under real-world constraints.
  • Collaborate across science and engineering teams to integrate solutions into deployment.

Skills

Python
Java
C++
Applied mathematics
Statistics
Machine learning

Education

Advanced degree in engineering/CS/ML/robotics/OR statistics or equivalent

Tools

PyTorch
TensorFlow
NumPy
SciPy
scikit-learn
Pandas

Job description

Amazon's Science Software team is seeking an Applied Scientist to blend ML, applied mathematics, and production software across computer vision, optimization, and orchestration. You will frame ambiguous business problems as tractable scientific ones and build models, prototypes, and real-time systems.

You will collaborate with multiple science and engineering teams to deploy solutions in a scalable architecture, working on edge devices and on-premise platforms within a fast-paced fulfillment

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