Applied Scientist, Geospatial & Safety Science
Amazon - Bellevue, Washington, USA
Posted Feb 26, 2026
Benefits
- Parental leave
- 6 weeks From the posting source checked Jun 20, 2026
- Non-birth-parent leave
- 6 weeks From the posting source checked Jun 20, 2026
- Family-building benefits
- Mental health support
- Offered From the posting source checked Jun 20, 2026
- Relocation assistance
- Not verified
- Childcare support
- Offered From the posting source checked Jun 20, 2026
- Learning budget
- Not verified
- Verification
- Source-linked checked Jun 7, 2026
- Salary
- $143K-$193K From the posting source checked Jun 20, 2026
- 401(k) match
- Reported from DOL Form 5500 industry filing (not employer-specific)
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Market context
- U.S. role benchmark (BLS OEWS)
- $111,944 U.S. median for this role
- Projected growth (BLS Employment Projections)
- +13.7% - Much faster than average
50% above the BLS role benchmark for data and ml aggregate.
Matched to SOC 15-1252 - Data and ML aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Role
Schedule
- Shift type
- Not verified
- Weekend work
- Not verified
Company
Application
- Cover letter
- Not verified
- Assessment
- Not verified
- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Applied Scientist, Geospatial & Safety Science Bellevue, Washington, USA Amazon's Last Mile Geospatial Science team leverages sate of the art computer vision, generative AI, and deep learning to enhance vehicle navigation and ensure safe, efficient deliveries by enriching map data from billions of satellite, aerial, and street-level images and videos. The role involves building large-scale machine learning systems that analyze terabytes of multimodal data to solve novel problems, translating business requirements into prototypes while prioritizing driver and customer safety. The team seeks scientists who can combine domain expertise with machine learning to invent and implement state-of-the-art solutions in a collaborative environment with direct business impact. Key job responsibilities Successful candidates should have a deep knowledge (both theoretical and practical) of various machine learning algorithms for large scale computer vision problems, the ability to map models into production-worthy code, the communication skills necessary to explain complex technical approaches to a variety of stakeholders and customers, and the excitement to take iterative approaches to tackle big, long term problems. The applied scientist should be proficient with image and video analysis using machine learning, including designing architecture from scratch, modify existing loss functions, full model training, fine-tuning, and evaluating the latest deep learning models. The applied scientist optimizes different models for specific platforms, including edge devices with restricted resources. Generative AI, Multi-modal models, e.g., Large Vision Language Models, zero-shot, few-shot, and semi-supervised learning paradigms are used extensively. Basic Qualifications: - 3+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience
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