Amazon.com Services LLC

Technology

AppliedScientist,WHS

$143–193k Bellevue, Washington, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Applied Scientist, WHS at Amazon.com Services LLC. Skills: Computer vision, Machine learning, Deep learning, Model deployment. Design computer vision models. Develop computer vision models”

What You'll Achieve.

Reduce safety incidents globally; Improve people's lives

Industry & Context.

Technology
Problems you'll solve

Root cause analysis

What They're Looking For.

Must Have

3+ years building models, PhD or Master's degree, 4+ years CS, CE, ML, Experience in patents or publications, Experience programming Java, C++, Python, Experience algorithms and data structures, Experience parsing, Experience numerical optimization, Experience data mining, Experience parallel and distributed computing, Experience high-performance computing

Nice to Have

Experience using Unix/Linux, Experience professional software development, Experience training and deploying ML systems, Experience machine learning technologies, Experience Reinforcement Learning, Experience Deep Learning, Experience Computer Vision, Experience Natural Language Processing

What You'll Do.

Design computer vision models

Develop computer vision models

Deploy computer vision models

Design machine learning models

Develop machine learning models

Deploy machine learning models

Develop model architectures

Run experiments on datasets

Collaborate with software engineers

Optimize for inference latency

Optimize for accuracy

Optimize for reliability

Analyze operational data

Identify safety risk patterns

Translate findings into improvements

Stay current with research

Evaluate research applicability

Communicate technical approaches

Contribute to scientific culture

How You'll Work.

Team & Collaboration

Cross-functional teams; Partner with engineers; Partner with safety experts

Communication Scope

Technical documentation; Presentations; Design reviews

Full Job Description

We're looking for an Applied Scientist to develop computer vision and machine learning models that keep Amazon's workforce safe. Your research and models will be deployed across hundreds of operations facilities globally, helping to reduce safety incidents for over 1.5 million people. You'll join a team where science meets real-world impact. You'll design and train models for tasks like activity recognition, anomaly detection, object detection, and risk prediction using video, image, and sensor data from Amazon's operational environments. You'll work closely with software engineers to take your models from experimentation through production deployment at scale. If you're excited about applying advanced ML research to a problem that genuinely improves people's lives, and you thrive in an environment where your work ships to production, not just to a paper, this is the role for you. Key job responsibilities - Design, develop, and deploy computer vision and machine learning models for workplace safety applications (e.g., activity recognition, anomaly detection, pose estimation, object detection) - Develop and iterate on model architectures using deep learning frameworks, running experiments on large-scale video, image, and sensor datasets - Collaborate with software engineers to productionize models - optimizing for inference latency, accuracy, and reliability in edge and cloud environments - Analyze operational data to identify patterns and signals indicating safety risks, and translate findings into actionable model improvements - Stay current with the latest research in computer vision, deep learning, and related fields, and evaluate applicability to safety use cases - Communicate findings and technical approaches clearly to both technical and non-technical stakeholders through documents, presentations, and design reviews - Contribute to the team's scientific culture through code reviews, knowledge sharing, and mentorship About the team Amazon's Workplace Health & S

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