Applied Scientist II — ML & Physics‑Driven Optimization

Socket.dev; Boston (MA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Paid time off

Job summary

Amazon's fulfillment technology team seeks an Applied Scientist to translate research into scalable production systems across perception platforms and optimization tasks within the fulfillment network.

The role frames business problems as scientific challenges and builds ML systems, first-principles models, and edge deployments for real-world constraints, shaping the scientific direction of a growing organization.

Qualifications

• PhD in engineering, CS, ML, robotics, OR statistics or equivalent quantitative field.
• 1+ years of building ML models for business applications.
• Experience with Java, C++, Python or related language.
• Experience with PyTorch or TensorFlow and the scientific Python stack.

Responsibilities

• Design, develop, and deploy ML and scientific solutions spanning classical machine learning, statistical modeling, computer vision, optimization, and physics-informed modeling in production environments.
• Rapidly ramp on unfamiliar problem domains, frame ambiguous business problems as tractable scientific challenges, and prototype solutions end to end.
• Author or co-author research findings for internal or external peer-reviewed venues, and provide peer feedback on research procedures and results across teams.
• Prototype and evaluate sensing hardware and lightweight, edge-deployable models that run on commodity compute under real-world constraints.
• Collaborate across multiple science and engineering teams to integrate your solutions into deployment architecture, mentoring less experienced scientists along the way.

Skills

Machine learning

Statistics

Applied mathematics

ML model development

Education

PhD in engineering/quantitative field

Tools

PyTorch

TensorFlow

NumPy

SciPy

scikit-learn

pandas

Job description

Amazon's fulfillment technology team seeks an Applied Scientist to translate research into scalable production systems across perception platforms and optimization tasks within the fulfillment network.

The role frames business problems as scientific challenges and builds ML systems, first-principles models, and edge deployments for real-world constraints, shaping the scientific direction of a growing organization.