Applied Scientist, Computer Vision for Transportation Ops

Amazon Inc.

Bellevue (WA)

On-site

USD 143,000 - 193,000

Full time

7 days ago
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Benefits offered by this job

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

Job summary

Amazon Transportation Services is seeking an Applied Scientist to advance computer vision across the Middle Mile network. You will build, train, and productionize CV models for asset inspection, detection, and visual understanding, working with scientists and engineers to drive measurable business impact in Bellevue, WA.

The role emphasizes hands-on model development, rigorous experiments, and deploying scalable pipelines to production, with a strong focus on real-world CV challenges and

Qualifications

  • 3+ years building ML models for business applications.
  • PhD or Master\'s with 4+ years in CS/CE/ML or related field.
  • Experience developing and implementing deep learning algorithms for CV.
  • Experience in professional software development.

Responsibilities

  • Develop and productionize computer vision models across a portfolio of use cases.
  • Collaborate with scientists, product owners, and engineering teams to validate approaches.
  • Design rigorous experiments with measurable business impact.
  • Build training and inference pipelines and deploy models to production.
  • Frame problems from first principles and select appropriate methods.

Skills

Machine learning
Computer vision
Programming
Software development

Education

PhD or Master\'s + 4+ years

Tools

Java
C++
Python

Job description

Amazon Transportation Services is seeking an Applied Scientist to advance computer vision across the Middle Mile network. You will build, train, and productionize CV models for asset inspection, detection, and visual understanding, working with scientists and engineers to drive measurable business impact in Bellevue, WA.

The role emphasizes hands-on model development, rigorous experiments, and deploying scalable pipelines to production, with a strong focus on real-world CV challenges and

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