Applied Science Intern: Reinforcement Learning & Optimization

Amazon Inc.; Seattle (WA); On-site; USD 122,000 - 149,000; Full time

Job summary

Amazon Science is seeking boundary-pushing graduate student scientists to turn revolutionary theory into awe-inspiring reality. You will conduct research into deep reinforcement learning and advanced optimization, collaborating with product managers, scientists, and software engineers.

You will explore Bayesian optimization, time series, multi-armed bandits and related areas, supported by state-of-the-art computing and mentorship from industry leaders. Positions are in multiple U.S.

Qualifications

• Enrolled in a PhD program and eligible for internship.
• Relocation to internship location required.
• Experience programming in Java, Python with PyTorch or JAX.
• Experience in Optimization, RL, Time Series, and related ML areas.
• Publication or thesis evidence of research depth.
• Available for full-time (40 hours per week) internship.

Responsibilities

• Develop scalable ML models and algorithms for RL and optimization.
• Design, development and evaluation of innovative ML models for business problems.
• Research and apply latest ML techniques from academia and industry.
• Collaborate with diverse teams and clearly communicate results.
• Leverage AI tools to accelerate research while critically reviewing outputs.

Skills

Optimization

Reinforcement Learning

Time Series

Large Language Models

Graph Modeling

Supervised Learning

Unsupervised Learning

Deep Learning

Predictive Modeling

Java

Python

PyTorch

JAX

Education

PhD candidate

Tools

PyTorch

JAX

Stable Baselines

RLlib

Custom RL frameworks

Job description

Amazon Science is seeking boundary-pushing graduate student scientists to turn revolutionary theory into awe-inspiring reality. You will conduct research into deep reinforcement learning and advanced optimization, collaborating with product managers, scientists, and software engineers.

You will explore Bayesian optimization, time series, multi-armed bandits and related areas, supported by state-of-the-art computing and mentorship from industry leaders. Positions are in multiple U.S.