Senior Applied Scientist, Model Customization, Generative AI Innovation Center, Model Customiza[...]

Amazon; City Of London; On-site; GBP 80,000 - 100,000; Full time

Job summary

A leading technology company is seeking an Applied Scientist to develop generative AI solutions. The role involves collaboration with customers and AI/ML teams to innovate solutions that deliver real-world impact. Ideal candidates will hold a PhD with relevant experience or a Master's with extensive hands-on practice in Python and AI methodologies. Flexibility and a supportive work culture are valued in this position.

Benefits

Mentorship and career growth opportunities

Work-life balance initiatives

Inclusive team culture

Qualifications

• PhD degree in a relevant field plus 5 years of experience or Master’s with 10 years.
• 5+ years of experience with Python to build and evaluate models.
• Experience in algorithms, data mining, and high-performance computing.

Responsibilities

• Collaborate with AI/ML scientists to develop generative AI solutions.
• Interact with customers to implement generative AI solutions.
• Provide feedback to help define product direction.

Skills

Python

Algorithms and data structures

Numerical optimization

Large Language Models

Education

PhD in computer science, engineering, mathematics, or related field

Master’s degree

Tools

Open-source frameworks

Job description

Job ID: 3095630 | AWS EMEA SARL (UK Branch)

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.

Key job responsibilities

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
• 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

About the team

The team is composed of varied experiences and fosters an inclusive culture that embraces difference.

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 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

• PhD degree in 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
• 5+ years of hands on experience with Python to build, train, and evaluate models
• 5+ years of 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

• Demonstrated experience with building LLM-powered agentic workflow, orchestration, and agent customization
• Experience with model optimization techniques (quantization, distillation, compression, inference optimization etc.)
• Experience with open-source frameworks for model customization like trl, verl, and for building LLM-powered applications like LangChain, LlamaIndex, and/ or similar tools
• Strong communication 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.
• Experience driving scientific agenda and technical strategy in a team, including building consensus on technical approaches and mentoring other scientists to improve their technical capabilities.

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult your Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.