AmazonSenior Applied Scientist, Frontier AI Assets - Assessments (FAI)
AI in this role
Design automated dataset quality assessment systems and self-improving AI agents for frontier model evaluation.
In this role you will build the automated systems that make quality assessment at scale. Large language model (LLM)-as-a-Judge is the starting point, and our goal is to develop an agentic system that plan its own audits, critiques its own judgments, and improves its own accuracy over time. You will design it, calibrate it against expert human judgment, and set the technical direction for how the organization measures data quality.
Key job responsibilities
- Design the assessment methodology for frontier AI assets
- Build the automated assessment system, from LLM-as-a-Judge baselines to agents that plan their own audits and learn from their own errors
- Oversee the expert audit program and raise the proficiency of the auditors who run it
- Design the measurement methods that make quality findings defensible, including sampling strategy and error analysis
- Communicate findings to the teams that create the data and to the teams that train models and build agents using it
- Set technical direction for assessment science, mentor junior scientists, and present results to senior leadership
- Publish research on assessment methodology at top-tier venues and contribute to patents
A day in the life
- Train AI agents to assess the quality of datasets and evaluation benchmarks, and document where they fall short
- Diagnose their failures and improve the models, rubrics, and calibration behind them
- Review the quality reports that auditors and agents produce, and decide whether the conclusions hold
- Coach expert auditors and junior scientists, and move more of the manual audit work into automation
- Lead research on self-improving assessment agents, from open problem to publication
About the team
FAI Assessment is part of Frontier AI Assets in AGI. We assess the quality of the datasets and benchmarks behind Amazon's frontier models and agents, and we define what good data means. Today that work relies on human-in-the-loop review by domain experts. Our aim is to automate it with self-improving agents that experts keep calibrated. We are a small team of scientists working closely with the data, modeling, and agent teams.
Basic qualifications
- 5+ years of building machine learning models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- PhD in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience building complex software systems that have been successfully delivered to customers
- Experience in machine learning, data mining, information retrieval, statistics or natural language processing, or experience designing or architecting (design patterns, reliability and scaling) of new and existing systems
Preferred qualifications
- Working knowledge of the public benchmarks used to evaluate frontier AI models and agents, and of their limitations
- Experience designing large-scale human data collection and improving the quality of the datasets
- Experience with the data pipelines behind model post-training and alignment
- Experience in developing recursive self-improving agents based on frontier LLMs
- Publication record at top-tier machine learning venues such as ICML, ICLR, CVPR, NeurIPS, AAAI, EMNLP, Interspeech, ICASSP
- Experience in an academic research or teaching position, such as professor, lecturer, or postdoctoral researcher
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, BELLEVUE - 167,100.00 - 226,100.00 USD annually
How we rate this
Senior Applied Scientist, Frontier AI Assets - Assessments (FAI) at Amazon rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- Tell me about a project where applied science was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where data quality was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI Agents, NLP, Applied Science, Machine Learning, and Data Quality. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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