Senior Applied Scientist, Proserve Generative Ai Innovation Center

**DESCRIPTION**:
The Generative AI Innovation Center at AWS helps AWS customers accelerate the use of Generative AI and realize transformational business opportunities. This is a cross-functional team of ML scientists, engineers, architects, and strategists working step-by-step with customers to build bespoke solutions that harness the power of generative AI.

As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.

We’re looking for ML Applied Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

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.

Key job responsibilities
As a Senior ML Applied Scientist, you will
- Collaborate with ML scientists and architects to research, design, develop, and evaluate state-of-the-art generative AI algorithms to address real-world challenges across industries
- Lead customer interactions to deeply understand the business problem, and help them in defining and implementing generative AI solutions and guide customers on adoption patterns and paths to production
- Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and develop new solutions
- Create and deliver best practice recommendations, scientific artifacts, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholders
- Effectively communicate complex ML results and insights to diverse audiences, translating technical concepts for non-technical stakeholders while providing appropriate depth for technical audiences in both public-facing publications and internal knowledge sharing sessions

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.

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 (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 we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

**BASIC QUALIFICATIONS**:

- Experience with neural deep learning methods and machine learning
- Hands on experience building models with deep learning frameworks like PyTorch, Tensorflow, or MxNet
- Strong communication skills, with attention to detail and ability to convey rigorous technical concepts and considerations to technical and non-technical audiences
- Fluency in written and spoken English

**PREFERRED QUALIFICATIONS**:

- Proven knowledge of AWS platform and tools
- Hands-on experience of building ML solutions on AWS
- Experience in professional software development
- Scientific thinking and the ability to invent, a track record of thought leadership and contributions that have advanced the field

m/w/d