Amazon Development Center U.S., Inc.
Posted Sep 20, 2026
Principal Applied Scientist, Humorphic Labs
Midtown · Onsite · Senior
Help moving to NYC
Visa sponsorship likely
Amazon Development Center U.S., Inc. filed 156 NYC H-1Bs, Oct 2025–Jun 2026; this is a technical role. This is historical evidence, not a promise of sponsorship.
Relocation assistance not stated
Relocation assistance is not stated in this posting.
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Pay
PostedNot stated
H-1B record$179K Company-wide NYC H-1B median offered wage at Amazon Development Center U.S., Inc.: $178,900 (156 filings, Oct 2025–Jun 2026). Not specific to this role.
About the role
AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset.
Humorphic Labs builds AI systems that work as teammates rather than tools. We ship them six months or more ahead of anyone else. The Lab is small on purpose so it can change direction in a day.
Humorphism concerns the quality of the working relationship between a person and an AI system. It asks whether the system acts proactively adapts to the person and the situation earns trust manages attention and strengthens human judgment. This role turns those behaviors into testable questions then answers them with working systems.
You will own the scientific agenda. You will convert a fuzzy behavioral goal into an end-to-end plan that covers data agent architecture evaluation and the product experiment that tests it. You will write code every week and build large parts of the experimental stack yourself because it does not exist yet.
The first focus areas are agentic products where a domain expert holds judgment the system cannot replace. Amazon Connect places an assistant beside a person handling a live conversation. AWS Bio Discovery places one beside a scientist running experiments. Both need evaluation that measures collaboration quality rather than task completion alone.
The role changes shape as the Lab matures. It starts as hands-on science in close partnership with product and engineering. Later it moves inside a product team to carry adoption of what the Lab proved.
Key job responsibilities
• Own the scientific strategy for human-AI collaboration across agentic and multimodal systems.
• Convert desired interaction behaviors into falsifiable hypotheses evaluation tasks and system requirements.
• Build the evaluation system that measures teammate behavior trust adaptation human contribution and failure recovery.
• Design agent systems that use memory tools planning and recovery then test them with the people who do the work.
• Design data collection and curation for language speech and interaction traces.
• Build significant parts of the experimental stack yourself because that stack does not exist yet.
• Diagnose failures across data models orchestration evaluation and product interaction.
• Define the requirements engineering needs to turn a proven method into a product capability.
• Partner with design product engineering and behavioral research from problem definition through product validation.
• Run experiments with partner product teams then report what worked what failed and what changed as a result.
• Set the standard for reproducible experiments evidence and scientific review inside the Lab.
• Mentor scientists and engineers without moving away from hands-on work.
• Represent the work in internal reviews and in appropriate external scientific venues.
A day in the life
Your week has two centers of gravity.
Most days you build. You take a claim about how a teammate should behave design the smallest experiment that can falsify it and run it. You read interaction traces from real sessions then argue with the engineers about what they mean.
The rest of the week belongs to the people the work is for. You sit with a contact center agent or a bench scientist and watch where the system helps and where it intrudes. You leave with the next hypothesis. Often you leave with evidence that kills the last one.
About the team
Amazon values diverse experiences. Even if you do not meet all of the preferred 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.
Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud.
Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity and AmazeCon conferences, inspire us to never stop embracing our uniqueness.
We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
- PhD in computer science, machine learning, artificial intelligence, or a related technical field, or a Master's degree with equivalent applied science experience, or an equivalent body of work
- Experience developing agentic AI systems or large language model applications through an end-to-end product cycle
- Experience designing evaluation for systems that have no standard benchmark
- Experience writing substantial research or production code in Python or a comparable language
- Experience leading complex scientific work across engineering and product partners
- Evidence of independent decisions in ambiguous, consequential technical environments
- Experience with post-training methods, including supervised fine-tuning and reinforcement learning
- Experience with multimodal models across language, speech, and vision
- Experience with speech systems or real-time conversational systems
- Experience measuring collaboration quality, trust, adaptation, or human contribution in AI systems
- Experience building agent systems that use memory, tools, planning, and recovery mechanisms
- Experience founding a scientific program, or joining an early team before that team had established its methods
- A record of scientific influence through publications, patents, open-source work, or deployed systems
- Experience mentoring senior scientists and engineers without moving away from hands-on work
- Experience partnering with design, product, and behavioral research from problem definition through product validation
- Experience taking a proven method into a product team and staying until that team adopted it
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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, TX, Austin - 198,900.00 - 269,000.00 USD annually
Humorphic Labs builds AI systems that work as teammates rather than tools. We ship them six months or more ahead of anyone else. The Lab is small on purpose so it can change direction in a day.
Humorphism concerns the quality of the working relationship between a person and an AI system. It asks whether the system acts proactively adapts to the person and the situation earns trust manages attention and strengthens human judgment. This role turns those behaviors into testable questions then answers them with working systems.
You will own the scientific agenda. You will convert a fuzzy behavioral goal into an end-to-end plan that covers data agent architecture evaluation and the product experiment that tests it. You will write code every week and build large parts of the experimental stack yourself because it does not exist yet.
The first focus areas are agentic products where a domain expert holds judgment the system cannot replace. Amazon Connect places an assistant beside a person handling a live conversation. AWS Bio Discovery places one beside a scientist running experiments. Both need evaluation that measures collaboration quality rather than task completion alone.
The role changes shape as the Lab matures. It starts as hands-on science in close partnership with product and engineering. Later it moves inside a product team to carry adoption of what the Lab proved.
Key job responsibilities
• Own the scientific strategy for human-AI collaboration across agentic and multimodal systems.
• Convert desired interaction behaviors into falsifiable hypotheses evaluation tasks and system requirements.
• Build the evaluation system that measures teammate behavior trust adaptation human contribution and failure recovery.
• Design agent systems that use memory tools planning and recovery then test them with the people who do the work.
• Design data collection and curation for language speech and interaction traces.
• Build significant parts of the experimental stack yourself because that stack does not exist yet.
• Diagnose failures across data models orchestration evaluation and product interaction.
• Define the requirements engineering needs to turn a proven method into a product capability.
• Partner with design product engineering and behavioral research from problem definition through product validation.
• Run experiments with partner product teams then report what worked what failed and what changed as a result.
• Set the standard for reproducible experiments evidence and scientific review inside the Lab.
• Mentor scientists and engineers without moving away from hands-on work.
• Represent the work in internal reviews and in appropriate external scientific venues.
A day in the life
Your week has two centers of gravity.
Most days you build. You take a claim about how a teammate should behave design the smallest experiment that can falsify it and run it. You read interaction traces from real sessions then argue with the engineers about what they mean.
The rest of the week belongs to the people the work is for. You sit with a contact center agent or a bench scientist and watch where the system helps and where it intrudes. You leave with the next hypothesis. Often you leave with evidence that kills the last one.
About the team
Amazon values diverse experiences. Even if you do not meet all of the preferred 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.
Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud.
Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity and AmazeCon conferences, inspire us to never stop embracing our uniqueness.
We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
- PhD in computer science, machine learning, artificial intelligence, or a related technical field, or a Master's degree with equivalent applied science experience, or an equivalent body of work
- Experience developing agentic AI systems or large language model applications through an end-to-end product cycle
- Experience designing evaluation for systems that have no standard benchmark
- Experience writing substantial research or production code in Python or a comparable language
- Experience leading complex scientific work across engineering and product partners
- Evidence of independent decisions in ambiguous, consequential technical environments
- Experience with post-training methods, including supervised fine-tuning and reinforcement learning
- Experience with multimodal models across language, speech, and vision
- Experience with speech systems or real-time conversational systems
- Experience measuring collaboration quality, trust, adaptation, or human contribution in AI systems
- Experience building agent systems that use memory, tools, planning, and recovery mechanisms
- Experience founding a scientific program, or joining an early team before that team had established its methods
- A record of scientific influence through publications, patents, open-source work, or deployed systems
- Experience mentoring senior scientists and engineers without moving away from hands-on work
- Experience partnering with design, product, and behavioral research from problem definition through product validation
- Experience taking a proven method into a product team and staying until that team adopted it
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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, TX, Austin - 198,900.00 - 269,000.00 USD annually