Machine Learning Engineer

US, VA, Virtual Location - Virginia

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Job summary
This position requires candidates to be located in the United States and hold or be able to acquire a US Security Clearance.

Are you a hands-on Engineer focused on Data and/or Machine Learning who can make a huge impact on a dynamic, fast moving business? Do you want to help Public Sector Customers deploy mission-critical data and ML solutions at scale? Are you detail-oriented and creative? Do you like to collaborate with others to achieve goals? Then this is the position for you.

AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. Here, you will get to work at an innovative company with talented teammates to directly enable public sector and non-profit customers – and have a lot of fun doing it! A successful candidate will be a person who enjoys diving deep into designing MLOps workflows, engineering modeling data pipelines, and building production-grade scalable Computer Vision inference capabilities. It will be a person who loves to learn and wants to help build real world solutions to leverage Machine Learning.

The AWS Professional Services Public Sector Data & ML team is primarily virtual, though there is an option to work on-site at any AWS Corporate office. Employees can live anywhere in the United States. This position involves up to 25% travel.

Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have twelve employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Work/Life Balance
Our team also puts a high value on work-life balance. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here, which is why we aren’t focused on how many hours you spend at work or online. Instead, we’re happy to offer a flexible schedule so you can have a more productive and well-balanced life—both in and outside of work.

Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.

Key job responsibilities
You will help design, build and operate Computer Vision capabilities using AWS services, leveraging best practices to run data pipelines and computer vision models on elastic, serverless and server-based cloud infrastructure. You will also help customers optimize their MLOps, from model re-training to model management to inference. You'll focus on operational excellence by implementing and integrating DevOps best practices, such as infrastructure as code, automated testing, and configuration management into cloud-native ML solutions.

Basic Qualifications

  • Programming experience with at least one modern language such as C++, C#, Java, Python, Golang, PowerShell, Ruby
  • 2+ years of non-internship professional software development experience
  • 1+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.

· 4+ years of professional software development experience
· 4+ years of programming experience with at least one modern language such as C++, C#, Java, or Python
· 2+ years of experience designing and deploying production-grade system architectures for Machine Learning
· Bachelors degree in Computer Science, Engineering, or related STEM Subjects, or equivalent experience
· Ability to acquire a US Security Clearance
· Experience deploying and maintaining Machine Learning models in production environments.
· Experience with containerization technologies such as Docker
· Experience designing and building highly-available distributed systems, and operating processes that reduce manual efforts and increase overall efficiency
· Experience designing and implementing software DevOps practices, including code standards, source control management, testing and deployment
· Experience with DevOps practices and tools for continuous delivery, infrastructure as code, software deployment automation, and configuration management

Preferred Qualifications

· Masters Degree in Computer Science, Engineering, or related STEM field, or equivalent experience
· A currently held US SECRET or TS clearance
· AWS Certifications, for example AWS Solution Architect Associate/Professional, Developer Associate, ML Specialty, and/or DevOps Engineer
· Experience deploying Computer Vision models (specifically Neural Networks) into production environments
· Familiarity with AWS services such as EC2, Cloud Development Kit (CDK) and/or CloudFormation, SageMaker, and S3
· Familiarity with container orchestration technologies such as Kubernetes, preferable services such as AWS ECS and EKS
· Experience developing automation to solve problems at scale
· Experience interfacing directly with customers to scope technical solutions


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

* Salary range is an estimate based on our salary survey at
Job regions: Remote/Anywhere North America
Job country: United States
Job stats:  11  0  0

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