The Amazon Web Services (AWS) US Federal Professional Services team is looking for a passionate and talented Computer Vision engineer who will collaborate with other scientists and engineers to develop computer vision and remote sensing capabilities to address customer use-cases at enterprise scale. If you are excited to work with massive amounts of data and computer vision models to solve real world challenges, this is the position for you! We work directly with public sector entities, medical centers, and non-profits to achieve their mission goals through the adoption of Machine Learning (ML) methods. We apply computer vision to numerous imagery and sensor types, such as satellite imagery, medical imaging, aerial video, synthetic aperture radar, X-Ray, and more! Amazon has been investing in Machine Learning for decades, and by joining AWS you’ll 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, providing direct and immediate impact for your customers. You will take the lead in planning, designing, and running experiments, researching new algorithms, and will work closely with talented data scientists and engineers to put algorithms and models into practice to help solve our customers' most challenging problems. You will also guide teams in the development of new solutions and aid customers in adopting AWS ML capabilities.

This position may involve local travel up to 25%.

This position requires that the candidate selected be a US Citizen and obtain and maintain an active TS/SCI security clearance with polygraph.

Key job responsibilities
In this role, you will:

* Engage directly with customers to understand their business problems and aid them in implementing their ML solutions.
* Deliver Machine Learning projects from beginning to end. This includes understanding the business need, planning the project, aggregating & exploring data, building & validating predictive models, and deploying completed ML capabilities on the AWS Cloud to deliver business impact for the customer.
* Use Deep Learning frameworks like PyTorch and Tensorflow to help our customers build computer vision models.
* Work on TB scale datasets, creating scalable, robust and accurate computer vision systems in versatile application fields.
* Work with other Professional Services Data Scientists and Machine Learning Engineers to help our customers operationalize ML capabilities
* Collaborate with Cloud Architects to build secure, robust, and easy-to-deploy cloud-native machine learning solutions.
* Work closely with customer account teams, scientific research teams and product engineering teams to optimize model implementations and deploy cutting-edge internal algorithms for your customers.
* Assist customers with Machine Learning Operations (MLOps) workflows such as model deployment, retraining, testing, and performance monitoring.
* Experience applying best practices from core Software Development activities to Machine Learning (deployability, unit testing, well structured extensible software, etc.)

About the team
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.

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.

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.

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.

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.

BASIC QUALIFICATIONS

- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience

PREFERRED QUALIFICATIONS

- Experience working with at least one of the following industry standard formats in an imagery domain: Satellite Imagery (NITF, GeoTIFF, SICD, etc.), Motion Imagery (commercial and USG FMV specs), or medical imagery (e.g. DICOM)
- Hands-on experience with state-of-the-art object detection approaches
- Experience managing multiple AWS and ML Environments through Infrastructure as code (Cloudformation, Cloud Development Kit, Terraform, Pulumi, etc.)
- 1+ years of experience with AWS services like SageMaker, S3, Fargate, DynamoDB, and/or Rekognition and 2+ years of experience handling terabyte-scale datasets
- Experience containerizing/deploying computer vision models, specifically neural networks, into production environments
- Experience designing and deploying cloud-native, enterprise-scale machine learning solutions in the AWS Cloud or with another major cloud provider
- Experience developing automation to solve problems at scale; track record of deploying and managing models in production

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