Some of the science challenges we work on include building Agentic AI systems for complex use cases such as optimizing customers' cloud architectures, innovating optimization techniques for cost-efficiency in agentic workflows, fine-tuning language models for domain specific use cases, building scalable agentic evaluation systems, designing continual learning systems that incorporate real-time customer and operational feedback to improve model performance over time and developing collaborative filtering approaches that surface personalized recommendations at scale.
You will have an opportunity to lead, invent, and design technology that will directly impact every customer across all AWS services. We are building industry-leading technology that cuts across a wide range of ML techniques from Natural Language Processing to Deep Learning and Generative Artificial Intelligence. You will be a key driver in taking something from an idea to an experiment to a prototype and finally to a live production system.
As a senior scientist on this team, you will define the science vision and long-term research roadmap for your problem space. You will set technical and research direction and ensure our approaches remain at the frontier of what's possible. You will mentor junior scientists and engineers, helping them grow their technical depth, develop scientific rigor, and navigate ambiguous problem spaces. You will raise the bar for the team through code reviews, science experimentation reviews, and by fostering a culture of experimentation and intellectual curiosity.
Key job responsibilities
- Deliver real world production systems at AWS scale.
- Work closely with the business to understand the problem space, identify the opportunities and formulate the problems.
- Use machine learning, data mining, statistical techniques, Generative AI and others to create actionable, meaningful, and scalable solutions for the business problems.
- Analyze and extract relevant information from large amounts of data and derive useful insights.
- Work with software engineering teams to deliver production systems with your ML models
- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
A day in the life
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.
BASIC QUALIFICATIONS
- PhD, or Master's degree and 6+ years of applied research experience
- 3+ years of building machine learning models for business application experience
- Knowledge of programming languages such as C/C++, Python, Java or Perl
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Bellevue - 167,100.00 - 226,100.00 USD annually
