Applied Scientist, AGI - Web \u0026 Knowledge Services

Amazon's AGI Web & Knowledge Services group is seeking a passionate, talented, and inventive Applied Scientist to lead the development of industry-leading Information retrieval systems. As part of our cutting-edge AGI-IR team, you will play a pivotal role in developing efficient AI solutions for a multi-modal future at scale.

In this role, you will work alongside renowned researchers and engineers to enable our customers to seamlessly interact with unstructured and semi-structured content through advanced capabilities like question answering, contextual search, and multi-turn dialogues. Your work will directly impact our customers in the form of products and services that make use of various machine learning, deep learning and language model technologies.

Key job responsibilities

- As an Applied Scientist, you will leverage your technical expertise and experience to demonstrate leadership in tackling large complex problems, setting the direction and collaborating with applied scientists and engineers to develop novel algorithms and modeling techniques to enable timely, relevant and delightful conversations.

- Leverage Amazon's large-scale data and computing resources to accelerate advances in the state of the art.

- Work backwards from customer needs and use that information to make trade-offs between different modeling approaches

- Collaborate with software engineering teams to integrate successful experimental results into complex Amazon production systems

- Report results to technical and business audiences in a manner that is statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment

- Drive best practices, helping to set high scientific and engineering standards on the team

- Promote the culture of experimentation and applied science at Amazon

BASIC QUALIFICATIONS

- Experience programming in Java, C++, Python or related language

- PhD, or Master's degree and 3+ years of CS, CE, ML or related field experience

- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

- 1+ years of building models for business application experience

PREFERRED QUALIFICATIONS

- PhD in Electrical Engineering, Computer Sciences, or Mathematics with specialties in natural language processing, recommendation system, or information retrieval

- Hands on experience with Visual LLMs

- Publications at peer-reviewed NLP/ML conferences (e.g. ACL, EMNLP, NAACL, NeurIPS, ICLR, ICML, AAAI, CVPR)

- Scientific thinking and the ability to invent, a track record of thought leadership and contributions that have advanced the field

- Experience with distributed systems, web services, or large-scale data processing.

- Good written and spoken communication skills

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.

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