AmazonPosted 3d ago

Applied Scientist, Generative AI and Synthetic Data

Applied Scientist, Generative AI and Synthetic Data at Amazon scores 95 out of 100 on AI centrality, which makes it a Level 4 role on this board.

US, CA, Sunnyvaleseniorfull-time$172k-$222k

AI in this role

Develop generative models, 3D simulation pipelines, and synthetic data methodologies to train AI systems for Amazon devices.

pythonpytorchdiffusion-modelsmultimodal-models

computer-visionmachine-learninggenerative-aisynthetic-data3d-spatial-understandingsimulationresearch

Amazon Lab126 builds the synthetic data that trains the AI in Amazon's devices, spanning wearables, smart home, and streaming. The team is looking for Applied Scientists to advance the science of making synthetic data maximally useful to the models it trains, across video, audio, and 3D. This is generation, but it is also the full loop around it: deciding what data to generate through data-centric methods like importance sampling and curriculum design, combining it with real data, and measuring its effect on the models that ship to customers.

A large part of the opportunity lies in the physical environment and how it responds to activity around it, including fast-moving, rule-governed events where signals across modalities must stay aligned in space and time. You will develop generative and simulation methods, from diffusion and multimodal models to physics-based digital twins and sim-to-real transfer, grounded in precise 3D spatial understanding, and deliver science artifacts that feed the models shipping in Amazon's devices. This is a hands-on research role with technical lead scope, reporting to a manager within a cross-discipline organization of Applied Scientists and Software Development Engineers.

Key job responsibilities
- Develop and fine-tune generative models and 3D simulation pipelines across video, audio, and sensor modalities, advancing temporal coherence, identity preservation, controllability, and causality so that generation is action-conditioned and physically plausible.
- Develop training recipes that combine synthetic and real data effectively, including mixing ratios, curriculum ordering, and sampling strategies, and characterize when synthetic data helps, when it hurts, and why.
- Build evaluation methods that predict synthetic data's impact on downstream models without full training runs, using proxy signals and embedding-based diagnostics that shorten the feedback loop from days to minutes.
- Model structured, rule-governed activity so that generated multimodal data stays coherent across modalities, 3D space, and time, and build 3D-consistent scene and event representations that keep generated data spatially faithful.
- Validate all work by measured improvement on production models, ship science artifacts to products, and mentor junior scientists and interns.

A day in the life
You might start your morning reviewing experiment results from an overnight training run, then join a design discussion with engineers to plan how your model will be integrated into a production pipeline. After lunch, you could be prototyping a new approach to a problem your team recently identified, writing clean and well-documented code as you iterate. Later, you might pair with a teammate to review their research methodology or prepare findings for an upcoming science review.

About the team
Our team owns the synthetic data strategy that accelerates time to market across Amazon's devices business. Demand for this data is growing, and we are actively expanding into new product categories. Our goal is to own this strategy across multiple flagship product lines for the entire devices organization, dramatically reducing how long it takes to get new capabilities into customers' hands.

We are a cross-discipline group of Applied Scientists and Software Development Engineers who partner closely to move ideas from research to production. If you want your science to directly shape the AI powering the next generation of Amazon devices, this is an exciting place to build your career.

Basic qualifications

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
- 2+ years of building machine learning models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience in designing experiments and statistical analysis of results

Preferred qualifications

- Experience with generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF
- Experience with popular deep learning frameworks such as MxNet and Tensor Flow
- Have publications on top-tier conferences, such as CVPR, ICCV, ECCV or NeurIPS

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, Sunnyvale - 171,600.00 - 222,200.00 USD annually
USA, WA, Bellevue - 142,800.00 - 193,200.00 USD annually

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Computer VisionMachine LearningGenerative AISynthetic Data3d Spatial UnderstandingSimulationResearchPython

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where generative ai was part of your work. What did you do?
Tell me about a project where synthetic data was part of your work. What did you do?
Tell me about a project where 3d spatial understanding was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Computer Vision, Machine Learning, Generative AI, Synthetic Data, and 3d Spatial Understanding. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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