A Machine Learning Engineer within the AWS Professional Services organization responsible for architecting, designing, and scaling AI/ML solutions for customers, helping them navigate AI transformation journeys.
Locations (2)
Arlington, Virginia, United States
Herndon, Virginia, United States
Compensation
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.
Responsibilities
Designing and implementing complex, scalable, and secure AI/ML solutions on AWS tailored to customer needs
Developing and deploying machine learning models and generative AI applications that solve real-world business problems
Collaborating with customer stakeholders to identify high-value AI/ML use cases, gather requirements, and propose strategies for implementation
Providing technical guidance on applying AI, machine learning, and generative AI responsibly and cost-efficiently
Provides the daily reporting foundation for cross-functional teams, building and maintaining operational dashboards for storefront health, discovery metrics, and commercial trends across worldwide markets.
Responsibilities
Build and maintain operational dashboards that track storefront health, discovery metrics, and commercial trends across worldwide markets.
Monitor recurring performance reports, identifying trends and anomalies to help analysts and regional partners investigate root causes quickly.
Partner with Product, Editorial, BD and Growth Enablement teams to translate project needs into structured reporting solutions.
Track campaign milestones and featured placements against agreed goals.
Fulfill ad-hoc data requests from regional storefront managers.
Requirements
Bachelor's degree in business, finance, operations, information systems, or a related field.
Applied Scientist focused on causal inference, statistics, and machine learning to optimize marketing channels and product performance within Apple Services.
Responsibilities
Design, develop, and deploy causal inference and AI/ML solutions for marketing effectiveness
Define technical vision, strategy, and roadmap for AIML initiatives
Collaborate with cross-functional partners to deliver customer and business value
Requirements
Master’s degree in Statistics, Economics, Mathematics, Machine Learning, Computer Science, Engineering, or a related technical field
Nice to have: PhD in related field
Nice to have: Hands‑on experience leveraging Generative AI to improve productivity and generate new insights
The role will be embedded within a core business area (growth, product, AI and automation, or strategic partnerships) to own the analytical function end-to-end.
Locations (4)
New York, New York, United States
Palo Alto, California, United States
Chicago, Illinois, United States
Toronto, Ontario, Canada
Responsibilities
Define the right problems and understand what business question an analysis is answering
Define metrics, build reports, run analyses, design experiments, and surface data insights
Bring recommendations to counterparts in product, growth, finance or operations
The Data Scientist will be embedded within a core business area (growth marketing, product delevop, AI and automation, or strategic partnerships) owning the analytical function end-to-end.
Locations (4)
New York City, New York, United States
Palo Alto, California, United States
Chicago, Illinois, United States
Toronto, Ontario, Canada
Responsibilities
Define the right problems and understand what business question it's answering
Define metrics, build reports, run analyses, design experiments
Surface data insights that move team's key metrics
The successful candidate’s starting pay will fall within the pay range listed on this job posting, determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Ranges are market-dependent and may be modified in the future. In addition to base salary, the compensation may include opportunities for equity grants.
Responsibilities
Define the right problems and metrics for business units
Technical Program Manager, AI Infrastructure Programs
Scope, plan, and manage large programs, including management of associated financial and human resources, quality of software development and features, risks, schedules, communication plans, and deliverables.
Provide management with forecasts of project deliverables and detailed status updates.
Present your team's analyses, recommendations, and approaches to stakeholders and executive management.
Initiate new software development programs, identify and deliver software features that drive performance, scale, functionality, security, quality, and efficiency, based on prioritized needs.
Be a catalyst for program support and delivery.
Requirements
Bachelor's degree in a technical field
Nice to have: Master's degree in Computer Science or Engineering
Nice to have: 8 years of experience managing cross‑functional or cross‑team projects
Nice to have: 8 years of technical program management or engineering experience in large‑scale distributed systems, data center software, or cloud infrastructure
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.
Responsibilities
Designing and implementing complex, scalable, and secure AWS solutions tailored to customer needs
Providing technical guidance and troubleshooting support throughout project delivery
Collaborating with stakeholders to gather requirements and propose effective migration strategies
Acting as a trusted advisor to customers on industry trends and emerging technologies
Sharing knowledge within the organization through mentoring, training, and creating reusable artifacts
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