Key job responsibilities
- Lead and manage a team of applied scientists and analysts, setting the strategic direction and roadmap for scientific research that influences organizational goals and annual planning processes.
- Partner with business and technical stakeholders to define vision, priorities, and success criteria - ensuring your team builds the right solutions at the right level of fidelity and that final outputs are ready to inform business decisions or move to production.
- Evaluate and improve machine learning model accuracy and performance using rigorous experimentation, feature engineering, and hyperparameter optimization, while establishing a team culture focused on reproducibility and scientific rigor.
- Hire, develop, and mentor scientists and technical contributors, providing growth opportunities and empowering team members to take ownership of key workstreams and deliver results independently.
- Identify opportunities for new analysis, efficiency improvements, and generative AI integration, allocating resources effectively and proactively mitigating risks before they become roadblocks.
A day in the life
You start your morning reviewing experiment results with your scientists, asking probing questions about model assumptions and business relevance. Mid-morning, you join a cross-functional meeting with engineering and product partners to align on priorities for an upcoming launch. After lunch, you conduct a one-on-one with a team member, coaching them on a new research proposal. Later, you draft a narrative summarizing your team's quarterly progress and outline next steps for leadership review. Throughout, you balance hands-on technical guidance with strategic planning to keep your team delivering high-quality science.
About the team
Sales AI is focused on applying scientific research and machine learning to solve real problems that matter to Amazon and its advertising customers. We value intellectual curiosity, collaboration, and a commitment to building inclusive, high-performing teams. We believe that great science happens when people with different perspectives work together toward a shared mission. As we continue to grow, you will play a key role in shaping the team's direction, expanding our capabilities, and ensuring our work has lasting impact across the business.
BASIC QUALIFICATIONS
- 3+ years of scientists or machine learning engineers management experience
- Knowledge of ML, NLP, Information Retrieval and Analytics
- PhD, or Master's degree and 6+ years of applied research experience
- Knowledge of machine learning approaches and algorithms
PREFERRED QUALIFICATIONS
- Experience building machine learning models or developing algorithms for business application
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
- Experience with autonomous AI services and associated model development
- Experience with sequential recommendation, user intent/mission modeling, or behavioral modeling
- Experience with LLM training, fine-tuning, or adaptation (e.g., tokenizer modification, domain adaptation)
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 - 183,800.00 - 248,700.00 USD annually
