Structured reasoning:** Design LLM prompting architectures that reliably transform operational data into executive-quality narrative summaries, decision framings, and recommendation rationales
LLM evaluation:** Build evaluation frameworks that measure LLM output quality (accuracy, actionability, calibration) and detect degradation over time
RAG systems:** Design retrieval-augmented generation systems that ground LLM outputs in operational data, historical playbooks, and institutional knowledge
Progressive autonomy:** Design the trust-calibration system where AI gradually earns expanded authority based on demonstrated accuracy over time
Research & Production
End-to-end ownership:** Take models from research through production deployment — you ship, you monitor, you iterate
Experimentation:** Design A/B tests and quasi-experiments to validate model improvements and measure business impact
Stakeholder communication:** Translate complex scientific results into actionable insights for non-technical senior leaders
3+ years of building machine learning models for business application experience
PhD in Machine Learning, Statistics, Computer Science, Operations Research, or related quantitative field (or Master's + 4 years of applied science experience)
Strong expertise in at least two of: causal inference, time-series forecasting, anomaly detection, NLP/LLMs
Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn, statsmodels)
Experience with experimental design and causal methods (difference-in-differences, synthetic control, instrumental variables, or Bayesian causal inference)
Experience deploying ML models to production (not just research/notebooks)
Track record of publications or equivalent internal research contributions
Experience in building machine learning models for business application
Experience with LLM integration (prompt engineering, RAG, fine-tuning, evaluation frameworks)
Experience with dose-response modeling, treatment effect estimation, or pharmacometric-style modeling
Experience with operational/infrastructure data (time-series at scale, noisy signals, multi-dimensional hierarchies)
Experience with Bayesian methods (probabilistic programming, uncertainty quantification)
Background in supply chain optimization, capacity planning, or operations research
Experience building decision support systems that serve non-technical stakeholders
Experience measuring GenAI/productivity tools' causal impact on workflows
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
