Applied Scientist: ML, Vision & Optimization in Production

Amazon; Hagerstown (MD); On-site; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Paid time off

Parental leave

Job summary

Amazon is seeking an Applied Scientist for Computational Modeling to lead ML and first-principles modeling in production environments across a broad set of problems. You will frame ambiguous business needs, prototype end-to-end solutions, and contribute to edge-deployable models and hardware sensing prototypes.

The role emphasizes collaboration with science and engineering teams, rapid onboarding to new domains, and building scalable ML systems with strong product focus in material handling

Qualifications

• Currently pursuing or holding an advanced degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics or equivalent quantitative field
• Experience with programming languages such as Python, Java, C++
• Strong foundation in applied mathematics, statistics, and machine learning, with versatility across domains
• Experience with PyTorch or TensorFlow and the scientific Python stack (NumPy, SciPy, scikit-learn, pandas)

Responsibilities

• Own the research and development of scientific and ML solutions across ML, CV, optimization, and physics-informed modeling in production.
• Rapidly ramp on unfamiliar problem domains and frame business problems as tractable scientific problems.
• Prototype and evaluate sensing hardware and edge-deployable models on commodity compute.
• Collaborate across multiple science and engineering teams to integrate solutions into deployment architecture.

Skills

Python

Java

C++

ML fundamentals

Education

Advanced degree in engineering/CS/ML/robotics/statistics

PhD

Tools

PyTorch

TensorFlow

NumPy/SciPy/pandas

scikit-learn

Job description

Amazon is seeking an Applied Scientist for Computational Modeling to lead ML and first-principles modeling in production environments across a broad set of problems. You will frame ambiguous business needs, prototype end-to-end solutions, and contribute to edge-deployable models and hardware sensing prototypes.

The role emphasizes collaboration with science and engineering teams, rapid onboarding to new domains, and building scalable ML systems with strong product focus in material handling