Success in any organization begins with its people and having a comprehensive understanding of our workforce and how we best utilize their unique skills and experience is paramount to our future success. WISE (Workforce Intelligence powered by Scientific Engineering) delivers the scientific and engineering foundation that powers Amazon's enterprise-wide workforce planning ecosystem. Addressing the critical need for precise workforce planning, WISE enables a closed-loop mechanism essential for ensuring Amazon has the right workforce composition, organizational structure, and geographical footprint to support long-term business needs with a sustainable cost structure.
We are looking for a passionate, talented, and inventive Machine Learning Engineer (MLE) to join our ML/AI team to work on Advanced Optimization and LLM solutions. You will partner with Applied Scientists, Software Engineers, Data Engineers, TPMs, Product Managers and Senior Management to help create world-class solutions. We're looking for people who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history. You will leverage your knowledge in ML/AI to work with other engineers to investigate design approaches, prototype new technology and evaluate technical feasibility. You will have end-to-end ownership of operational and technical aspects of the insights you are building for the business, and will play an integral role in strategic decision-making. Your expertise will be invaluable in defining data strategies, enrichment processes, model optimizations, and evaluation methods that will set new standards in the industry!
Key job responsibilities
- Work closely with Applied scientists to process data, scale machine learning models while optimizing
- drive crisp and timely execution of milestones, consider and advise on key design and technology trade-offs with engineering teams
- define, implement and continuously improve delivery and operational efficiency
- interface with and influence your stakeholders, balancing business needs vs. technical constraints and driving clarity in ambiguous situations
- develop and monitor model health metrics, anticipate and clear blockers, manage escalations
