Senior Applied Scientist, Model Customization, Generative AI Innovation Center, Model Customiza

Job  | Amazon Web Services, Inc.

Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step‑by‑step with customers to build bespoke solutions that harness the power of generative AI.

Starting in 2024, the Innovation Center launched a new Custom Model and Optimization program to help customers develop and scale highly customized generative AI solutions. The team helps customers imagine and scope bespoke use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop and optimize models to power their solutions, and make plans for launching solutions  GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently.

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 Applied Scientists capable of using GenAI and other techniques to design, evangelize, and implement state‑of‑the‑art solutions for never‑before‑solved problems.

• Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real‑world challenges.
• Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production.
• Help customers optimize their solutions through approaches such as model selection, training or tuning, right‑sizing, distillation, and hardware optimization.
• Provide customer and market feedback to product and engineering teams to help define product direction.

Diverse Experiences: AWS values diverse experiences. Even if you do not meet all of the preferred 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.

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.

AWS values curiosity and connection. Our employee‑led and company‑sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continuous innovation is fueled by bold ideas, fresh perspectives, and passionate voices from our teams.

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.

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.

• 3+ years of building machine learning models for business application experience.
• PhD, or Master’s degree and 6+ years of applied research experience.
• Experience programming in Java, C++, Python or related language.
• Experience with neural deep‑learning methods and machine learning.

• Experience with modeling tools such as R, scikit‑learn, Spark…