Applied Scientist III, AWS Startups

About the role

AWS Startups supports hundreds of thousands of founders globally - from first credit to scaled production workload. Data and machine learning are how we identify high-potential startups early, personalize the guidance we deliver, and decide where to invest next.

We recently launched AWS Startup Advisor, an AI-powered service that brings AWS expertise directly into the developer tools founders already use, and we need a scientist to build the ML capabilities that power its proactive recommendations.

In this role, you will own science problems end-to-end - from the data foundation that unifies signals about founders and their products, through model design and evaluation, to production deployment serving hundreds of thousands of startups. If you want your models to reach real founders the same week you ship them, this is the role.

Key job responsibilities

• Own the science for problem areas end-to-end: frame the problem, define the data strategy, build and evaluate ML models for recommendation systems, startup segmentation, and fraud detection, and deploy them into production.
• Apply generative AI and large language models to personalize the technical guidance founders receive, including retrieval, ranking, and evaluation of LLM-powered experiences.
• Design and run experiments that keep model quality quantified and defensible, making clear trade-offs between accuracy, latency, and cost as you balance rapid iteration with production reliability.
• Partner with product, engineering, design, and go-to-market teams to translate science into scalable products, and communicate results clearly to both technical and non-technical leaders.
• Raise the scientific bar through design and code reviews, mentor other scientists and engineers, and contribute to the broader scientific community through publications or peer reviews.

A day in the life

You might start the morning digging into a messy dataset to uncover a new segmentation signal, then shift to reviewing an A/B test that measures how a recommendation model is changing founder engagement. After lunch you could pair with an engineer to optimize inference latency for a fraud-detection model, then join a product review where you present trade-offs between two ranking approaches for AWS Startup Advisor. Expect to regularly move between hands-on modeling work and cross-team conversations that shape what gets built next.

Basic Qualifications

• 3+ 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
• Experience with neural deep learning methods and machine learning

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with LLMs, foundation models, or generative AI, including prompt engineering, fine-tuning, retrieval-augmented generation, or agentic architectures

Additional information

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.

Pay and benefits

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.

USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually