Principal Applied Scientist, Generative AI Innovation Center

Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.

The Generative AI Innovation Center helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization.

As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities.

You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.

We’re looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.

AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector.

The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success.

AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services.

Key job responsibilities

The primary responsibilities of this role are to :

• Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries
• Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them
• Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solution

About the team

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.

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

Arlington, VA, USA Atlanta, GA, USA Boston, MA, USA Chicago, IL, USA Houston, TX, USA Miami, FL, USA New York, NY, USA San Diego, CA, USA San Francisco, CA, USA Seattle, WA, USA

BASIC QUALIFICATIONS

7+ years of building models for business application experience

PhD degree and 10+ years, or Master's degree and 15+ years of CS, CE, ML or related field experience

Experience in patents or publications at top-tier peer-reviewed conferences or journals

Experience programming in Java, C++, Python or related language

Experience with neural deep learning methods and machine learning

PREFERRED QUALIFICATIONS

PhD degree in computer science, engineering, mathematics, operations research, or in a highly quantitative field

Practical experience in solving complex problems in an applied environment

Hands on experience building models with deep learning frameworks like MXNet, Tensorflow, or PyTorch

Strong communication skills, with attention to detail and ability to convey rigorous mathematical concepts and considerations to non-experts

Comfortable working in a fast paced, highly collaborative, dynamic work environment

Scientific thinking and the ability to invent, a track record of thought leadership and contributions that have advanced the field.