Machine Learning Engineer
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Job Description
About the Company:
Amazon is revolutionizing customer service through cutting-edge AI and ML. The Device, Digital and Alexa Support (D2AS) team is responsible for customer service operations and solutions for all Amazon Devices and Digital products (Kindle, Echo, Amazon Music, Prime Video, etc.). They strive to make digital experiences effortless for customers by anticipating, evaluating, preventing, and eliminating customer effort. The team combines strategic thinking, technology expertise, and customer experience best practices to provide the right support at the right time, tailored to each customer’s needs.
Job Description: Machine Learning Engineer
This role focuses on developing cutting-edge AI solutions using Large Language Models (LLM), Machine Learning (ML), and Natural Language Processing (NLP) to understand and resolve customer issues and provide exceptional customer service. The successful candidate will be passionate about learning, research, and deploying production-ready solutions in a collaborative environment. The work involves ideation, experimentation, iteration, optimization, and scaling while maintaining a balance between speed and quality.
Key Responsibilities:
• Utilizing ML and Generative AI tools (Amazon SageMaker, Amazon Bedrock) to create scalable production solutions that improve customer experience.
• Building, training, tuning, and deploying models, including data labeling.
• Collaborating with applied and data scientists to develop and fine-tune scalable ML and Generative AI solutions.
• Working directly with product stakeholders to understand business problems and assist in implementing their ML ecosystem.
• Analyzing and extracting information from large datasets to automate and optimize key processes.
• Collaborating with science and engineering teams to implement models and new algorithms.
A Day in the Life:
The work involves solving challenging problems and innovating for customers by pushing technological boundaries to create superior experiences. Data guides decisions, and collaboration with engineering, science, and product teams fosters an innovative learning environment.
Basic Qualifications:
• 3+ years of non-internship professional software development experience
• 2+ years of non-internship experience in designing or architecting (design patterns, reliability, and scaling) new and existing systems
• Experience programming with at least one software programming language
• Bachelor’s degree in computer science or equivalent
• 2+ years of relevant experience in developing and deploying large-scale machine learning or deep learning models and/or systems into production, including batch and real-time data processing
Preferred Qualifications:
• 3+ years of experience in the full software development lifecycle (coding standards, code reviews, source control management, build processes, testing, and operations)
• Master’s or PhD degree in computer science, engineering, mathematics, operations research, or a highly quantitative field
• Practical experience in solving complex problems in an applied environment
• Experience with AWS services (SageMaker, EMR, S3, DynamoDB, and EC2)
• Experience with machine learning, deep learning, NLP, CV, GNN, or distributed training
• Strong communication skills, attention to detail, and the ability to explain complex mathematical concepts to non-experts
• Ability to work in a fast-paced, collaborative, and dynamic environment
Compensation: The base pay ranges from $129,300/year to $223,600/year, depending on location and experience. Additional compensation may include equity, sign-on payments, and benefits.
