About this role
Description
Do you want to join a team of innovative scientists to research and develop Agentic AI technology that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment? Amazon Connect is an AI-first customer experience solution that enables superior outcomes at a lower cost. Since its 2017 public launch Amazon Connect has become an AI leader transforming how organizations of all types interact with their customers.
As a Sr. Applied Scientist on our team, you will work closely with senior technical and business leaders from within the team and across AWS. You distill insight from huge data sets, conduct cutting-edge research, and develop science solutions to conquer complex business problems, from conception to deployment. You own the measurement science behind your work — defining the offline evaluation harnesses and online experiments that prove an agentic system is actually better, and converging on a small number of trustworthy metrics that tie model quality to customer outcomes. You have a strong academic background and industry experience in machine learning and deep learning, with deep domain knowledge in generative AI and LLMs. You are comfortable quickly prototyping and iterating on your ideas to build and evaluate robust agentic systems using modern tooling such as PyTorch, vLLM, agent frameworks and MCP, and AWS services including Amazon Bedrock and SageMaker.
Learn more about Amazon Connect here: https://aws.amazon.com/connect/
About the team
Our team is leading AI/ML research and development in Amazon Connect. We use state-of-the-art science and engineering practices to address the hard problems in contact center for our customers, and we move fast to implement solutions and refine them based on customer feedback.
Basic Qualifications
- 5+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Demonstrated experience with generative AI and LLMs — fine-tuning, prompting, retrieval, or post-training.
Preferred Qualifications
- PhD, or a PhD or equivalent research experience and experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience in automation or monitoring frameworks, deployment or development
- Experience designing evaluation frameworks for open-ended or subjective tasks where ground truth is expensive or unavailable, including synthetic data generation.
- Experience with agentic and multi-turn systems, including RL-based post-training (RLHF/RLAIF/GRPO or similar), environment simulation, or agent harness evaluation.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits .
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually