Applied Scientist — ML, Vision & Optimization (Edge)

Socket.dev; Boston (MA); Hybrid; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

RSUs

Paid time off

Parental leave

Job summary

Amazon is seeking an Applied Scientist to drive research-to-production ML solutions across computer vision, optimization, and edge-enabled systems. You will frame complex business problems into tractable scientific efforts and prototype novel ML systems and first-principles models for scalable impact.

In this role, you will collaborate with diverse science and engineering teams to integrate models into our deployment architecture, optimizing performance in both prototype and production

Qualifications

• Pursuing or holding an advanced degree in a quantitative field.
• Proficiency in Python, Java and C++ in production ML systems.
• Strong foundation in ML, statistics and applied mathematics across domains.
• Experience with PyTorch or TensorFlow and the scientific Python stack (NumPy, SciPy, scikit-learn, pandas).

Responsibilities

• Own research and development of ML solutions across ML, CV, optimization and physics-informed modeling in production.
• Frame ambiguous business problems as tractable scientific problems and prototype end-to-end solutions.
• Prototype and evaluate edge-deployable models on commodity hardware within real-world constraints.
• Collaborate across science and engineering teams to integrate solutions into deployment architectures.

Skills

Python

C++

Java

PyTorch

TensorFlow

NumPy

SciPy

scikit-learn

pandas

Education

Advanced degree in engineering, technology, computer science, machine learning, robotics, statistics or equivalent

Tools

PyTorch

TensorFlow

NumPy

SciPy

scikit-learn

pandas

Embedded/edge compute

Job description

Amazon is seeking an Applied Scientist to drive research-to-production ML solutions across computer vision, optimization, and edge-enabled systems. You will frame complex business problems into tractable scientific efforts and prototype novel ML systems and first-principles models for scalable impact.

In this role, you will collaborate with diverse science and engineering teams to integrate models into our deployment architecture, optimizing performance in both prototype and production