Pay
$118K–$195K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,828 postings in the last year. Stated ranges, not salaries paid.
Senior Applied Scientist, Applied AI & Data Architecture
New York, New York, USA
Actively hiring, first seen by us 5 hours ago- Pay
Multiple pay amounts — pay source
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, CA, Irvine - 167,100.00 - 226,100.00 USD annually USA, CO, Denver - 167,100.00 - 226,100.00 USD annually USA, NY, New York - 183,800.00 - 248,700.00 USD annually USA, TX, Dallas - 167,100.00 - 226,100.00 USD annually USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
Read the full posting- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: full-time
From the employer’s posting
What you’ll work on
Full postingYou will work closely with AWS service teams to close the loop between what customers need and what AWS builds.
You will work closely with the service teams so that the patterns we prove in the field become native features.
From the employer’s posting
The role splits roughly 50/50 between direct customer engagements and building leverage through reusable science assets, open source, and thought leadership. Customer work: You will partner with customer science and engineering teams on their hardest customization problems. You'll frame the problem, choose the right approach, design the evaluation, and work hands-on to get customized models into production. - Building time : You will develop reference implementations and training recipes, contribute to open-source projects, and publish findings that raise the bar for the broader community. You will work closely with AWS service teams to close the loop between what customers need and what AWS builds. You will be the team's scientific bar-raiser. You will set evaluation standards, review modeling choices, and publish what you learn, internally and externally. You will work closely with the service teams so that the patterns we prove in the field become native features.
Customer work: You will partner with customer science and engineering teams on their hardest customization problems. You'll frame the problem, choose the right approach, design the evaluation, and work hands-on to get customized models into production. - Building time : You will develop reference implementations and training recipes, contribute to open-source projects, and publish findings that raise the bar for the broader community. You will work closely with AWS service teams to close the loop between what customers need and what AWS builds. You will be the team's scientific bar-raiser. You will set evaluation standards, review modeling choices, and publish what you learn, internally and externally. You will work closely with the service teams so that the patterns we prove in the field become native features. This is a global role within the Solutions Architect organization. You will maintain deep relationships with AI service and science teams and collaborate with specialists and builder customers across regions to stay current on the state of the art and influence roadmaps.
Education & alternatives
About the team Applied AI and Data Architects (AIDA) is a team of specialized AI prototypers with a product mindset. The team builds a portfolio of domain-specific Tech Field Products in areas such as semantics, data engineering, ML, AI security/governance/agent-ops, and DevOps. Model customization and evaluation is one of the team's focus areas. Its goal is to make foundation and open weight model customization repeatable, measurable, and accessible to builders. Each product is grounded in customer reality. The team earns the right to guide by building alongside customers. Basic qualifications - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - 4+ years of building machine learning models for business application experience 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. - Experience customizing models for agentic use cases, such as tool use, multi-step reasoning, or reinforcement learning from environment or verifier feedback - Experience with model efficiency techniques such as quantization, distillation, and inference cost and latency optimization
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 13, 2026today
- Recorded sightings
- 1
- Employer says posted
- Nov 10, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
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Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- C++
- Java
- Python
- AWS
- Hadoop
- Spark
- NumPy
- scikit-learn
- SciPy
- TensorFlow
Where the posting names them
About the team Applied AI and Data Architects (AIDA) is a team of specialized AI prototypers with a product mindset. The team builds a portfolio of domain-specific Tech Field Products in areas such as semantics, data engineering, ML, AI security/governance/agent-ops, and DevOps. Model customization and evaluation is one of the team's focus areas. Its goal is to make foundation and open weight model customization repeatable, measurable, and accessible to builders. Each product is grounded in customer reality. The team earns the right to guide by building alongside customers. Basic qualifications - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - 4+ years of building machine learning models for business application experience 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. - Experience customizing models for agentic use cases, such as tool use, multi-step reasoning, or reinforcement learning from environment or verifier feedback - Experience with model efficiency techniques such as quantization, distillation, and inference cost and latency optimization
Do you want to define how enterprises customize their own models ? Do you enjoy work that spans data curation, post-training, evaluation, and production deployment of customized models? As a Senior Applied Scientist in Amazon Web Services (AWS), you will build that path from customization to trusted evaluation. You will prove it with customers on their own data. You will help customers decide when and how to customize foundation models, using techniques that range from parameter-efficient fine-tuning and distillation to preference optimization and reinforcement fine-tuning. You will turn that work into reusable recipes, evaluation methods, and open-source accelerators that let builders customize models reliably and cost-effectively. The role splits roughly 50/50 between direct customer engagements and building leverage through reusable science assets, open source, and thought leadership.
The role splits roughly 50/50 between direct customer engagements and building leverage through reusable science assets, open source, and thought leadership. - Customer work: You will partner with customer science and engineering teams on their hardest customization problems. You'll frame the problem, choose the right approach, design the evaluation, and work hands-on to get customized models into production. - Building time : You will develop reference implementations and training recipes, contribute to open-source projects, and publish findings that raise the bar for the broader community. You will work closely with AWS service teams to close the loop between what customers need and what AWS builds. You will be the team's scientific bar-raiser. You will set evaluation standards, review modeling choices, and publish what you learn, internally and externally. You will work closely with the service teams so that the patterns we prove in the field become native features.
This is a global role within the Solutions Architect organization. You will maintain deep relationships with AI service and science teams and collaborate with specialists and builder customers across regions to stay current on the state of the art and influence roadmaps. Key job responsibilities - Own the evaluation methodology. Design the evaluation methodology behind every model-selection and customization decision. Score quality, cost, latency, and safety in a way that is reproducible, statistically sound, and calibrated to each customer's quality bar. - Lead customization engagements end to end. Drive work from data curation and synthetic data generation through fine-tuning, distillation, continued pre-training, and reinforcement learning. Re-score every change against the same harness so customers see exactly what they gained. - Invent where no textbook solution exists. Develop new techniques such as reward design for agentic tasks, simulation-based evaluation, and efficient serving of small decision models, then package them as reusable code any Solutions Architect can run. - Turn engagement learnings into reusable assets. Build reference architectures, open-source accelerators, workshops, enablement materials, blogs, and papers that form a repeatable path from evaluation through deployment. - Advise field teams and customers. Act as the scientific advisor on trade-offs among prompting, retrieval, customization, and frontier models, including knowing when not to customize. - Mentor and influence across teams. Mentor engineers and Solutions Architects in ML methodology. Turn customer evidence into feature requests and proposals for AWS service teams that make field-proven patterns native product capabilities. A day in the life You start the morning reviewing a customer's distillation results, where a smaller model is closing the gap with a much larger one. The gains cluster in one task slice, so you flag possible data leakage before the design review. Afterward, you add a held-out evaluation set and a capability-regression check to the training recipe. In the afternoon, you share engagement findings with a product team to shape how evaluation fits into the customization workflow. You wrap up by refining an open-source evaluation harness you'll release alongside a customer workshop.
- PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - 4+ years of building machine learning models for business application experience 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. - Experience customizing models for agentic use cases, such as tool use, multi-step reasoning, or reinforcement learning from environment or verifier feedback - Experience with model efficiency techniques such as quantization, distillation, and inference cost and latency optimization Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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Demand for Data Scientist roles
New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
US government figures for data scientists
- Median yearly pay, May 2025
- $120,230
- Expected change, 2025 to 2035
- 34.6% more jobsagainst 3.5% for all jobs
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
Job description
Application deadline: Nov 15, 2026
Do you want to define how enterprises customize their own models ? Do you enjoy work that spans data curation, post-training, evaluation, and production deployment of customized models?
As a Senior Applied Scientist in Amazon Web Services (AWS), you will build that path from customization to trusted evaluation. You will prove it with customers on their own data. You will help customers decide when and how to customize foundation models, using techniques that range from parameter-efficient fine-tuning and distillation to preference optimization and reinforcement fine-tuning. You will turn that work into reusable recipes, evaluation methods, and open-source accelerators that let builders customize models reliably and cost-effectively.
The role splits roughly 50/50 between direct customer engagements and building leverage through reusable science assets, open source, and thought leadership.
- Customer work: You will partner with customer science and engineering teams on their hardest customization problems. You'll frame the problem, choose the right approach, design the evaluation, and work hands-on to get customized models into production.
- Building time : You will develop reference implementations and training recipes, contribute to open-source projects, and publish findings that raise the bar for the broader community. You will work closely with AWS service teams to close the loop between what customers need and what AWS builds.
You will be the team's scientific bar-raiser. You will set evaluation standards, review modeling choices, and publish what you learn, internally and externally. You will work closely with the service teams so that the patterns we prove in the field become native features.
This is a global role within the Solutions Architect organization. You will maintain deep relationships with AI service and science teams and collaborate with specialists and builder customers across regions to stay current on the state of the art and influence roadmaps.
Key job responsibilities
- Own the evaluation methodology. Design the evaluation methodology behind every model-selection and customization decision. Score quality, cost, latency, and safety in a way that is reproducible, statistically sound, and calibrated to each customer's quality bar.
- Lead customization engagements end to end. Drive work from data curation and synthetic data generation through fine-tuning, distillation, continued pre-training, and reinforcement learning. Re-score every change against the same harness so customers see exactly what they gained.
- Invent where no textbook solution exists. Develop new techniques such as reward design for agentic tasks, simulation-based evaluation, and efficient serving of small decision models, then package them as reusable code any Solutions Architect can run.
- Turn engagement learnings into reusable assets. Build reference architectures, open-source accelerators, workshops, enablement materials, blogs, and papers that form a repeatable path from evaluation through deployment.
- Advise field teams and customers. Act as the scientific advisor on trade-offs among prompting, retrieval, customization, and frontier models, including knowing when not to customize.
- Mentor and influence across teams. Mentor engineers and Solutions Architects in ML methodology. Turn customer evidence into feature requests and proposals for AWS service teams that make field-proven patterns native product capabilities.
A day in the life
You start the morning reviewing a customer's distillation results, where a smaller model is closing the gap with a much larger one. The gains cluster in one task slice, so you flag possible data leakage before the design review. Afterward, you add a held-out evaluation set and a capability-regression check to the training recipe. In the afternoon, you share engagement findings with a product team to shape how evaluation fits into the customization workflow. You wrap up by refining an open-source evaluation harness you'll release alongside a customer workshop.
About the team
Applied AI and Data Architects (AIDA) is a team of specialized AI prototypers with a product mindset. The team builds a portfolio of domain-specific Tech Field Products in areas such as semantics, data engineering, ML, AI security/governance/agent-ops, and DevOps. Model customization and evaluation is one of the team's focus areas. Its goal is to make foundation and open weight model customization repeatable, measurable, and accessible to builders. Each product is grounded in customer reality. The team earns the right to guide by building alongside customers.
- Experience programming in Java, C++, Python or related language
- 4+ years of building machine learning models for business application experience
- Experience with large scale distributed systems such as Hadoop, Spark etc.
- Experience customizing models for agentic use cases, such as tool use, multi-step reasoning, or reinforcement learning from environment or verifier feedback
- Experience with model efficiency techniques such as quantization, distillation, and inference cost and latency optimization
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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.
USA, CA, Irvine - 167,100.00 - 226,100.00 USD annually
USA, CO, Denver - 167,100.00 - 226,100.00 USD annually
USA, NY, New York - 183,800.00 - 248,700.00 USD annually
USA, TX, Dallas - 167,100.00 - 226,100.00 USD annually
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
Do you want to define how enterprises customize their own models ? Do you enjoy work that spans data curation, post-training, evaluation, and production deployment of customized models?
As a Senior Applied Scientist in Amazon Web Services (AWS), you will build that path from customization to trusted evaluation. You will prove it with customers on their own data. You will help customers decide when and how to customize foundation models, using techniques that range from parameter-efficient fine-tuning and distillation to preference optimization and reinforcement fine-tuning. You will turn that work into reusable recipes, evaluation methods, and open-source accelerators that let builders customize models reliably and cost-effectively.
The role splits roughly 50/50 between direct customer engagements and building leverage through reusable science assets, open source, and thought leadership.
- Customer work: You will partner with customer science and engineering teams on their hardest customization problems. You'll frame the problem, choose the right approach, design the evaluation, and work hands-on to get customized models into production.
- Building time : You will develop reference implementations and training recipes, contribute to open-source projects, and publish findings that raise the bar for the broader community. You will work closely with AWS service teams to close the loop between what customers need and what AWS builds.
You will be the team's scientific bar-raiser. You will set evaluation standards, review modeling choices, and publish what you learn, internally and externally. You will work closely with the service teams so that the patterns we prove in the field become native features.
This is a global role within the Solutions Architect organization. You will maintain deep relationships with AI service and science teams and collaborate with specialists and builder customers across regions to stay current on the state of the art and influence roadmaps.
Key job responsibilities
- Own the evaluation methodology. Design the evaluation methodology behind every model-selection and customization decision. Score quality, cost, latency, and safety in a way that is reproducible, statistically sound, and calibrated to each customer's quality bar.
- Lead customization engagements end to end. Drive work from data curation and synthetic data generation through fine-tuning, distillation, continued pre-training, and reinforcement learning. Re-score every change against the same harness so customers see exactly what they gained.
- Invent where no textbook solution exists. Develop new techniques such as reward design for agentic tasks, simulation-based evaluation, and efficient serving of small decision models, then package them as reusable code any Solutions Architect can run.
- Turn engagement learnings into reusable assets. Build reference architectures, open-source accelerators, workshops, enablement materials, blogs, and papers that form a repeatable path from evaluation through deployment.
- Advise field teams and customers. Act as the scientific advisor on trade-offs among prompting, retrieval, customization, and frontier models, including knowing when not to customize.
- Mentor and influence across teams. Mentor engineers and Solutions Architects in ML methodology. Turn customer evidence into feature requests and proposals for AWS service teams that make field-proven patterns native product capabilities.
A day in the life
You start the morning reviewing a customer's distillation results, where a smaller model is closing the gap with a much larger one. The gains cluster in one task slice, so you flag possible data leakage before the design review. Afterward, you add a held-out evaluation set and a capability-regression check to the training recipe. In the afternoon, you share engagement findings with a product team to shape how evaluation fits into the customization workflow. You wrap up by refining an open-source evaluation harness you'll release alongside a customer workshop.
About the team
Applied AI and Data Architects (AIDA) is a team of specialized AI prototypers with a product mindset. The team builds a portfolio of domain-specific Tech Field Products in areas such as semantics, data engineering, ML, AI security/governance/agent-ops, and DevOps. Model customization and evaluation is one of the team's focus areas. Its goal is to make foundation and open weight model customization repeatable, measurable, and accessible to builders. Each product is grounded in customer reality. The team earns the right to guide by building alongside customers.
Basic qualifications
- PhD, or Master's degree and 6+ years of applied research experience- Experience programming in Java, C++, Python or related language
- 4+ years of building machine learning models for business application experience
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.
- Experience customizing models for agentic use cases, such as tool use, multi-step reasoning, or reinforcement learning from environment or verifier feedback
- Experience with model efficiency techniques such as quantization, distillation, and inference cost and latency optimization
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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.
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, CA, Irvine - 167,100.00 - 226,100.00 USD annually
USA, CO, Denver - 167,100.00 - 226,100.00 USD annually
USA, NY, New York - 183,800.00 - 248,700.00 USD annually
USA, TX, Dallas - 167,100.00 - 226,100.00 USD annually
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
Source details
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Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- 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 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, CA, Irvine - 167,100.00 - 226,100.00 USD annually USA, CO, Denver - 167,100.00 - 226,100.00 USD annually USA, NY, New York - 183,800.00 - 248,700.00 USD annually USA, TX, Dallas - 167,100.00 - 226,100.00 USD annually USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
- Location & working pattern
New York, New York, USA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.