Machine Learning Engineer, Data Platform Engineering, WWPS U.S Federal ProServe

Are you excited to help the US Intelligence Community leverage the volume and variety of their data and enable Machine Learning in mission workflows? Do you have a knack for helping these groups understand the data architectures that support Machine Learning Operations (MLOps) and the consultative and leadership skills to launch a project on a trajectory to success? The Amazon Web Services (AWS) US Federal Professional Services team works directly with US Intelligence community agencies and other public sector entities to achieve their mission goals through the adoption of Machine Learning (ML) methods. We build data platforms that optimize all types of data for ML model training, scale inference, automate model improvement, and organize insights for analytics and reporting. Amazon has been investing in Machine Learning for decades, and by joining AWS you will join a community of scientists and engineers developing leading edge solutions for enterprise-scale data science applications.

In this customer facing position, you will architect and implement innovative AWS cloud-native ML solutions that achieve customer business outcomes. You will take the lead in inspecting, investigating, and understanding customer data sources. You'll design and run experiments, and research new algorithms. You'll work closely with talented data scientists and engineers to create data flows to and from models, and build data platforms that infuse ML into diverse missions.

This position may required local travel up to 25%

It is expected to work from one of the above locations (or customer sites) at least 1+ days in a week. This is not a remote position. You are expected to be in the office or with customers as needed.

This position requires that the candidate selected be a US Citizen and must currently possess and maintain an active TS/SCI security clearance with polygraph. The position further requires the candidate to opt into a commensurate clearance for each government agency for which they perform AWS work.

Key job responsibilities
- Ability to quickly learn cutting-edge technologies and algorithms in the fields of both Traditional and Generative AI to participate in our journey to build the best models.
- Responsible for the development and maintenance of key platforms needed for developing, evaluating and deploying models for real-world applications.
- Work with other team members to investigate design approaches, prototype new technology and evaluate technical feasibility.
- Work closely with Data scientists to process massive data and scale machine learning models while optimizing.

About the team
About AWS

Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.

Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.