Senior Applied Scientist, Generative AI InnovationCenter

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 at scale. The 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.

As an Applied Scientist, you

will

• Collaborate with AI / ML scientists and

architects to research, design, develop, and evaluate generative AI

solutions to address real-world challenges

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

feedback to product and engineering teams to help define product

direction

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 (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

computer science, engineering, mathematics, operations research, or

in a highly quantitative field plus 5 years of relevant experience,

or Master's degree plus 10 years of relevant work

experience

to build, train, and evaluate models

experience in any of the following areas : algorithms and data

structures, parsing, numerical optimization, data mining, parallel

and distributed computing, high- performance computing

2+ years demonstrated experience with Large Language Model (LLM)

and Foundational Model post-training, continual pre-training,

fine-tuning, or reinforcement learning techniques.

Scientific publication track record at top-tier AI / ML / NLP

conferences or journals

PREFERRED

QUALIFICATIONS

experience with building LLM-powered agentic workflow,

orchestration, and agent customization

model optimization techniques (quantization, distillation,

compression, inference optimization etc.)

with open-source frameworks for model customization like trl, verl,

and for building LLM-powered applications like LangChain,

LlamaIndex, and / or similar tools

skills, with attention to detail and ability to convey rigorous

technical concepts and considerations to non-experts

Demonstrated ability to identify and frame technical problems from

broad product-level and business-level problem areas.

Track record of leading the design, implementation and delivery of

scientifically- complex solutions that span multiple

teams.

technical strategy in a team, including building consensus on

technical approaches and mentoring other scientists to improve

their technical capabilities.