Senior AI Scientist – Seller Support Automation

Amazon; Seattle (WA); On-site; USD 167,100 - 226,100; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon in Seattle is seeking a Senior Applied Scientist to own end-to-end RL-based agent improvement systems, from problem formulation through production deployment and impact measurement.

You will design preference learning, reward modeling, and policy optimization for conversational agents, collaborating with teams across NLP, ML, and tools to deploy state‑of‑the‑art models. You will publish research and mentor junior scientists to raise the bar.

Qualifications

• 7+ years of applied research experience.
• 5+ years of building ML models for business applications.
• PhD or Master’s + 5 years of applied research.
• Proficient in Java, C++, Python or related language.
• Experience with neural deep learning methods.
• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, TensorFlow, NumPy, SciPy.
• Experience with design, wireframing, and prototyping tools.
• Experience leveraging generative AI tools to enhance workflow and craft prompts.

Responsibilities

• Own end-to-end research and development of RL-based agent systems from problem formulation to production deployment.
• Design approaches to preference learning, reward modeling, and policy optimization for conversational agents.
• Build and maintain evaluation frameworks for agent quality across usefulness, correctness, safety, and alignment.
• Collaborate with scientists across NLP, optimization, ML, and statistics.
• Partner with 10+ engineering teams to deploy models to production for sellers worldwide.
• Publish research at top venues and support it through mentorship and hiring.

Skills

Python

Java

C++

Machine learning

Reinforcement learning

NLP

TensorFlow

NumPy

Education

PhD

Master's + 5 yrs research

Tools

R

scikit-learn

Spark MLLib

MxNet

Tensorflow

NumPy

SciPy

Job description

Amazon in Seattle is seeking a Senior Applied Scientist to own end-to-end RL-based agent improvement systems, from problem formulation through production deployment and impact measurement.

You will design preference learning, reward modeling, and policy optimization for conversational agents, collaborating with teams across NLP, ML, and tools to deploy state‑of‑the‑art models. You will publish research and mentor junior scientists to raise the bar.