Applied Scientist: AGI & LLM Training Environments

Amazon Inc.; Sunnyvale, Northern (CA, KY); Hybrid; USD 172,000 - 222,000; Full time

Benefits offered by this job

RSUs

Health insurance

401(k) matching

Job summary

Amazon.com Services LLC is seeking an Applied Scientist with a strong deep learning background to help build the training environments that teach large language models to reason, use tools, and complete complex, multi-step tasks. You will design, build, and evaluate reinforcement learning environments (RL gyms) for LLM training, defining tasks and reward signals, and running environments at scale.

You will work closely with model training teams to understand where environments are most valuable

Qualifications

• 3+ years building models for business applications.
• PhD, or Master’s degree and 4+ years in CS/CE/ML or related field experience.
• Experience programming in Java, C++, Python or related language.
• Experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.

Responsibilities

• Design, build, and evaluate RL environments (RL gyms) for LLM training.
• Define tasks and reward signals; build infrastructure to run environments at scale.
• Measure transfer of environments to model capability and verify environment quality.
• Collaborate with model training teams to maximize value of environments.

Skills

3+ years experience

Java

C++

Python

Algorithms

Data structures

Numerical optimization

Parallel and distributed computing

High-performance computing

Education

PhD

Master's + 4+ years CS/CE/ML

Tools

Unix/Linux

Job description

Amazon.com Services LLC is seeking an Applied Scientist with a strong deep learning background to help build the training environments that teach large language models to reason, use tools, and complete complex, multi-step tasks. You will design, build, and evaluate reinforcement learning environments (RL gyms) for LLM training, defining tasks and reward signals, and running environments at scale.

You will work closely with model training teams to understand where environments are most valuable