Sr. Applied Scientist, Applied AI & Data Architecture

Amazon Web Services, Inc., Seattle, Washington +4

The role

Application deadline: Nov 15, 2026

Do you want to define how enterprises customize their own models ? Do you enjoy work that spans data curation, post-training, evaluation, and production deployment of customized models?

As a Senior Applied Scientist in Amazon Web Services (AWS), you will build that path from customization to trusted evaluation. You will prove it with customers on their own data. You will help customers decide when and how to customize foundation models, using techniques that range from parameter-efficient fine-tuning and distillation to preference optimization and reinforcement fine-tuning. You will turn that work into reusable recipes, evaluation methods, and open-source accelerators that let builders customize models reliably and cost-effectively.

The team

Applied AI and Data Architects (AIDA) is a team of specialized AI prototypers with a product mindset. The team builds a portfolio of domain-specific Tech Field Products in areas such as semantics, data engineering, ML, AI security/governance/agent-ops, and DevOps. Model customization and evaluation is one of the team's focus areas. Its goal is to make foundation and open weight model customization repeatable, measurable, and accessible to builders. Each product is grounded in customer reality. The team earns the right to guide by building alongside customers.

PhD, or Master's degree and 6+ years of applied research experience
Experience programming in Java, C++, Python or related language
4+ years of building machine learning models for business application experience
Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Experience with large scale distributed systems such as Hadoop, Spark etc.
Experience customizing models for agentic use cases, such as tool use, multi-step reasoning, or reinforcement learning from environment or verifier feedback
Experience with model efficiency techniques such as quantization, distillation, and inference cost and latency optimization

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position.

These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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, CA, Irvine – 167,100.00 - 226,100.00 USD annually

USA, CO, Denver – 167,100.00 - 226,100.00 USD annually

USA, NY, New York – 183,800.00 - 248,700.00 USD annually

USA, TX, Dallas – 167,100.00 - 226,100.00 USD annually

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

What you will do

Own the evaluation methodology. Design the evaluation methodology behind every model-selection and customization decision. Score quality, cost, latency, and safety in a way that is reproducible, statistically sound, and calibrated to each customer's quality bar.
Lead customization engagements end to end. Drive work from data curation and synthetic data generation through fine-tuning, distillation, continued pre-training, and reinforcement learning. Re-score every change against the same harness so customers see exactly what they gained.
Invent where no textbook solution exists. Develop new techniques such as reward design for agentic tasks, simulation-based evaluation, and efficient serving of small decision models, then package them as reusable code any Solutions Architect can run.
Turn engagement learnings into reusable assets. Build reference architectures, open-source accelerators, workshops, enablement materials, blogs, and papers that form a repeatable path from evaluation through deployment.
Advise field teams and customers. Act as the scientific advisor on trade-offs among prompting, retrieval, customization, and frontier models, including knowing when not to customize.
Mentor and influence across teams. Mentor engineers and Solutions Architects in ML methodology. Turn customer evidence into feature requests and proposals for AWS service teams that make field-proven patterns native product capabilities.

A day in the life

You start the morning reviewing a customer's distillation results, where a smaller model is closing the gap with a much larger one. The gains cluster in one task slice, so you flag possible data leakage before the design review. Afterward, you add a held-out evaluation set and a capability-regression check to the training recipe. In the afternoon, you share engagement findings with a product team to shape how evaluation fits into the customization workflow. You wrap up by refining an open-source evaluation harness you'll release alongside a customer workshop.

Sr. Applied Scientist, Applied AI & Data Architecture

The role

Sr. Applied Scientist, Applied AI & Data Architecture

The role

The team

What you will do

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