Senior Applied Scientist - Conversational Agent, Analytics And Data Management

DESCRIPTION

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising solutions that drive product discovery and sales. We deliver billions of ad impressions every single day on behalf of our advertisers. You'll work with us to help our Advertising teams make sense of the torrent of data produced by the advertising lifecycle.

We are using cutting edge generative AI to help teams generate insights faster based on our massive data lake. You will need to invent new techniques for metrics retrieval and SQL generation to ensure we're retrieving accurate and trusted data. You'll create feedback loops to ensure our solution is constantly evaluating itself and improving.

Being that this is for a conversational AI position, here is what our bot replied when we prompted it for a job description of who should help build it:

Role Overview:

We are looking for an exceptional applied scientist to join our team building SpektrBot, a conversational AI assistant that helps data engineers and analysts with their workflows. You will work closely with engineers and product managers to design, implement, and optimize natural language processing models like intent classification, named entity recognition, question answering, etc. that enable our Ads chatbot to understand user requests and have natural conversations.

Responsibilities:

• Study and understand data engineering and analytics workflows to design the right conversational experiences

• Research, design, and develop NLP/NLU models for intent classification, entity extraction, sentiment analysis etc.

• Continuously improve models through techniques like active learning, transfer learning etc.

• Optimize models for metrics like precision, recall, latency, interpretability etc.

• Implement models within overall bot architecture and integrate with backend systems

• Collaborate with engineers to productionize and monitor models

• Stay up-to-date on latest advancements in conversational AI research, specifically in LLMs (multi-agent, chain of thought, autonomous agents)

• Be familiar with optimizing retrievers in RAG architectures.

Key job responsibilities

You will test multiple foundational models and fine tune when appropriate. You will create feedback loops that will evaluate performance and improve our systems. You will optimize prompts for better responses from our LLMs. You will build tools to auto-curate metadata using LLMs.

A day in the life

You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.

About the team

We have a small scrappy team carved out from a large Ads wide data lake team. We are swimming in petabytes of data that we help the organization make sense of. Our team's mission is to help anyone in the Ads org find the data they need using only natural language. We are a supportive and collaborative team who iterates quickly and shares in each others' successes.

We are open to hiring candidates to work out of one of the following locations:

Seattle, WA, USA

BASIC QUALIFICATIONS

• 3+ years of building machine learning models for business application experience

• PhD, or Master's degree and 6+ years of applied research experience

• Experience with neural deep learning methods and machine learning

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

• Experience working with foundational large language models (LLMs), both with tuning and prompt engineering using a RAG approach.

PREFERRED QUALIFICATIONS

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

• Experience with large scale distributed systems such as Hadoop, Spark etc.

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 $260,000/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.