Applied Scientist: ML & First‑Principles Modeling

Amazon Science; Massachusetts; On-site; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Parental leave

Job summary

Amazon Science is seeking an Applied Scientist on the Science SW team to be a versatile generalist who collaborates with scientists and engineers to bring research to production across a broad portfolio of problems, from computer-vision perception platforms to building-wide optimization and orchestration.

This role blends the scientific application of ML and applied mathematics with a strong product focus, framing ambiguous business problems as tractable scientific problems and implementing

Qualifications

• Advanced degree in engineering, technology, computer science, ML, robotics, operations research, statistics or related quantitative field.
• Experience with programming languages such as Python, Java, C++.
• Strong foundation in applied mathematics, statistics and ML across multiple problem domains.
• Experience with PyTorch or TensorFlow and the scientific Python stack (NumPy, SciPy, scikit-learn, pandas).

Responsibilities

• Own research and development of scientific and ML solutions across classical ML, statistical modeling, CV, optimization, and physics-informed modeling in production.
• Ramp quickly on unfamiliar problem domains and frame ambiguous business problems as tractable scientific problems, prototyping end-to-end.
• Prototype and evaluate sensing hardware and edge-deployable models on commodity compute under real-world constraints.
• Collaborate across multiple science and engineering teams to integrate solutions into deployment architecture.

Skills

Python

Java

C++

PyTorch

TensorFlow

NumPy

SciPy

scikit-learn

pandas

Education

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

Job description

Amazon Science is seeking an Applied Scientist on the Science SW team to be a versatile generalist who collaborates with scientists and engineers to bring research to production across a broad portfolio of problems, from computer-vision perception platforms to building-wide optimization and orchestration.

This role blends the scientific application of ML and applied mathematics with a strong product focus, framing ambiguous business problems as tractable scientific problems and implementing