Applied Scientist II, WW Prime - AI/ML Science
About the role
with prose from description (first paragraph(s) up to "Key job responsibilities"). -Responsibilities
with- list of responsibilities (the bullet list after "Key job responsibilities").
-
Requirements
maybe for Basic Qualifications. -Preferred Qualifications
maybe as Skills? Could use Skills heading for preferred qualifications. Or we could have Qualifications heading for basic and Skills for preferred. The instruction says we can use Qualifications, Skills. We'll do:Requirements
for Basic Qualifications.Skills
for Preferred Qualifications. -Benefits
with prose about benefits. -Pay
with salary range. No Schedule. We must not repeat job title. No metadata like Job ID. Remove boilerplate like EEO, accommodation link, etc. Also remove duplicated lines (the responsibilities appear twice). We need to preserve all substantive information; do not invent or summarize away detail. So we need to include all responsibilities bullet points exactly as given (maybe deduplicate). The responsibilities list appears twice identical; we should include each bullet once. Let's extract responsibilities bullet points from the text: From after "Key job responsibilities" repeated: - Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. - Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. - Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. - Develop offline policy estimation tools and integrate with measurement systems/econometric models. - Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. - Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. - Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. - Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems. That's 9 bullets. Now Basic Qualifications: - 3+ years of building models for business application experience - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience - Experience programming in Java, C++, Python or related language - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing Preferred Qualifications: - Experience using Unix/Linux - Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms Benefits: The paragraph after "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." We should keep that prose, but remove the link? The instruction: remove boilerplate, duplicated lines, application instructions. The link to benefits might be considered application instruction? It's more info; but it's a link. Might be considered boilerplate? Probably safe to keep the description but drop the link. However we should preserve substantive information; the link is not substantive. We can omit the link. Pay: "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 ..." Actually benefits already covered. Then "USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually". So we need to include that salary range and mention sign-on payments and RSUs. Also mention that final comp based on experience etc. We'll create afor Pay section with that info. Now About the role: The initial description before "Key job responsibilities". Let's capture: "Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists developing models to model customer behavior and optimize the customer experience with Amazon Prime. This includes understanding who our customers are, long-term value of the Prime membership program, and creating the right personalized framework for content and subscription optimization. As an AI/ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions. There are numerous scientific and technical challenges you will get to tackle in this role, such as optimizing/fine-tuning GenAI/LLM solutions for Prime personalization, building GenAI foundation models, global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, using outcomes from causal Econometric modeling, and Reinforcement learning. As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of. You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, ClaudeCode), various AI/ML algorithms and techniques (Deep Learning, GenAI/LLMs, transformers, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques. Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. Develop offline policy estimation tools and integrate with measurement systems/econometric models. Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems." Note that after that paragraph, the responsibilities list repeats. So we should stop before the repeated responsibilities. The About the role section should include all that prose up to before the responsibilities list. However the prose includes some sentences that are also in responsibilities (like "Stay abreast of current literature..." etc). Those sentences appear both in the description and responsibilities. To avoid duplication, we could keep them in About the role but not duplicate in responsibilities. However the responsibilities list also includes those same sentences. The instruction: Preserve ALL substantive information; do NOT invent or summarize away detail. Remove duplicated lines. So we should deduplicate: if a sentence appears both in description and responsibilities, we should include it only once. Which section to put it? Probably responsibilities is the canonical list; we can keep responsibilities and omit those sentences from About the role to avoid duplication. However the About the role description also contains other details not in responsibilities (like the initial motivation, understanding customers, etc). We'll keep those. Thus we need to parse the description and remove sentences that exactly match any responsibility bullet. Let's list responsibility bullets again: 1. Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. 2. Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. 3. Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. 4. Develop offline policy estimation tools and integrate with measurement systems/econometric models. 5. Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. 6. Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. 7. Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. 8. Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. 9. Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems. Now the description includes many of these sentences verbatim. Let's scan the description paragraph: "Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists developing models to model customer behavior and optimize the customer experience with Amazon Prime. This includes understanding who our customers are, long-term value of the Prime membership program, and creating the right personalized framework for content and subscription optimization. As an AI/ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions. There are numerous scientific and technical challenges you will get to tackle in this role, such as optimizing/fine-tuning GenAI/LLM solutions for Prime personalization, building GenAI foundation models, global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI/LLMs, supervised/semi-supervised learning, deep learning, transformer architectures, using outcomes from causal Econometric modeling, and Reinforcement learning. As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of. You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Sagemaker, DynamoDB, S3, ClaudeCode), various AI/ML algorithms and techniques (Deep Learning, GenAI/LLMs, transformers, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques. Stay abreast of current literature in the field and advance/build novel science solutions leveraging SoTA solutions. Build and develop AI/ML models and supporting infrastructure at TB scale, in coordination with software engineering teams. Leverage Deep Learning and GenAI solutions for building foundation models and personalized optimization solution. Develop offline policy estimation tools and integrate with measurement systems/econometric models. Establish scalable, efficient, automated processes for large scale data analyses, science development, science validation and model implementation. Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. Use AI/machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. Design, develop and evaluate highly innovative models and statistical approaches to understand