Machine Learning Engineer II, Intl. Seller Growth
2 days ago
Job ID: 2688874 | Amazon.com Services LLC
Come and be part of the International Seller Services (ISS) Central Analytics Data Engineering (DE) team and work on solving cutting edge GenAI solutions We are a team of DEs who support Applied Scientists, Data Scientists, and Economists who experiment, research, and turn machine/deep learning and AI research into great products for our customers. ISS is seeking a smart, highly-motivated, and experienced ML Engineer to join our team. In this role, you'll help us create the right Data and ML infrastructure.
As a ML Data Engineer, you will provide technical expertise, lead ML engineering initiatives and build end-to-end analytical solutions that are highly available, scalable, stable, secure, and cost-effective. You carry projects from proof-of-concept to deployment and serving with a high standard for model maintenance and operations. You orchestrate complex and/or distributed modeling systems to unlock new ML capabilities for your customers.
You are passionate about working with huge unstructured and structured datasets and have experience with the organization and curation of data for analytics and model training. You have a strategic and long-term view on architecting advanced data ecosystems. You are experienced in building efficient and scalable data services and have the ability to integrate data systems with AWS tools and services to support a variety of customer use cases/applications.
You will have the opportunity to work with scalable model development tools using SageMaker, AWS, and Docker. You will bring in techniques for automating model training, evaluation, deployment, and monitoring using machine learning pipelines. You will work closely with researchers in building algorithms ranging from classical machine learning to state-of-the-art deep neural network models on diverse types of signals.
Key job responsibilitiesIn this role, you have the opportunity to:
- Design, implement and operate large-scale, high-volume, high-performance data structures for analytics and data science.
- Develop and deploy models and pipelines that scale.
- Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions with a flexible and adaptable data architecture.
- Collaborate with Applied Scientists, Data Scientists, and Data Engineers to help adopt best practices in ML system creation, Experimentation Setup, and documentation.
- Identify opportunities in existing data/ML solutions for improvements.
Example projects:
- Setting up a Dev environment for experimenting with multiple embedding models for RAG setup.
- Implement a robust experimentation platform to test, iterate, and optimize the Conversation Assistants' performance across key RAG metrics.
- Developing reusable cloud infrastructure and deployment patterns to accelerate productionalization.
- Integrating disparate ML solutions into cohesive customer experiences.
Experience with machine learning techniques such as pre-processing data, training and evaluation of classification and regression models, and statistical evaluation of experimental data.
2+ years of non-internship professional ML-software development experience.
Programming experience with at least one modern language such as Python, Java, C++, or C# including object-oriented design.
2+ years of experience contributing to the architecture and design (architecture, design patterns, reliability, and scaling) of new and current systems.
Experience in building production quality and large scale deployment of applications related to NLP and ML.
PREFERRED QUALIFICATIONSAdvanced knowledge of performance, scalability, enterprise system architecture, and engineering best practices.
Academic and/or industry experience with one or more of the following domains: computer vision, deep learning, machine learning, or large-scale distributed systems.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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