Machine Learning Engineer , AWS Product ANalytics & DAta Science (PANDAS) Full-time

Do you want to transform millions of customer’s experience of interacting with AWS products using artificial intelligence and machine learning? Do you want to see the impacts of your work moving the needles on the billions dollars of AWS business? Do you want to stay on the cutting edge of technology (e.g. Gen AI, graph neural network, reinforcement learning, and forecasting models) to build scalable ML products that help service teams grow? The AWS Product Analytics and Data Science (PANDAS) team is at the forefront of leveraging cutting-edge AI/ML technology and infrastructure to redefine how internal product teams interact with and derive insights from their data.

Our vision is to use artificial intelligence and machine learning to enable AWS product teams and business leaders to drive product growth and create personalized, optimized, and simplified product experience. We strive to improve customers’ product experience, directly influence AWS’s top line and bottom line, and help AWS business leaders drive product growth. We want to be a centralized ML platform team that democratizes ML capabilities to AWS product teams and transform their product and customer experience.

You will work cross-functionally, typically collaborating with several teams of applied scientists, data engineer, product managers, and others in order to influence the business and technical strategy for a complex, high-performance organization. You will also drive impactful, long-term choices on system architecture, spearhead a high-quality engineering culture, leading the MLOps best practices across the org.

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Key job responsibilities

– Work collaboratively with cross-functional teams, including applied scientists, product managers, software engineers, and data scientists, to ensure ML solutions meet business objectives and enhance product offerings

– Develop ML models to support ML products for key business initiatives to improve customer’s product experience

– Deploy machine learning platform and infrastructure into production environments, integrating them seamlessly with existing systems and applications

– Work with our data engineers to build and manage our data pipelines to ensure scientists have timely and high quality data in production

– Continuously monitor and evaluate model performance, employing appropriate metrics to validate and refine models.

– Drive the MLOps best practices and roadmap to explore and implement new methodologies, tools, and techniques to drive innovation and improve the effectiveness of ML solutions

A day in the life

As a machine learning engineer, you will work closely with your customers and stakeholders to understand their business requirements, translating them into technical solutions. You will work with cross-functional team including applied scientists, product managers, and data engineers. You will build robust engineering pipelines and ML operation platform to support ML models. You will understand how ML model works and build ML models when needed. You will build model monitoring mechanism to understand model performance ensure our model meets SLA. You will spend time in architecture design and review meetings to hold high standard of our infrastructure.

About the team

We are a team of scientists and engineers supporting AWS product leaders to make high impact decisions through sophisticated analytical frameworks, trusted data science methods, and scalable ML products. We came from diverse backgrounds from statistics, computer science, engineering, and business analytics. We specialize in the full end to end ML development process, including data ingestion, ETL, model development, and model deployment in production. On the AWS Product Analytics team you will be surrounded by people that are exceptionally talented, bright, and driven. We are supporting the data science needs across AWS EC2, Database & Analytics, and other AWS service teams.

We are open to hiring candidates to work out of one of the following locations:

Seattle, WA, USA

BASIC QUALIFICATIONS

– 3+ years of non-internship professional software development experience

– 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience

– Experience programming with at least one software programming language

PREFERRED QUALIFICATIONS

– 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience

– Master’s degree in computer science or equivalent

– Experience in machine learning, data mining, information retrieval, statistics or natural language processing

– 3+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience

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. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,300/year in our lowest geographic market up to $223,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.