OPT Applied AI Engineer Jobs
Applied AI Engineer jobs are among the most actively sponsored roles for F-1 OPT students, with employers regularly filing H-1B visa petitions for engineers who build and deploy production AI systems. Most roles require a degree in computer science, machine learning, or a related field, and STEM OPT extension eligibility is nearly universal in this category.
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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.
LOCATION
USA, CA, Sunnyvale - 171,600.00 - 222,200.00 USD annually
USA, WA, Bellevue - 142,800.00 - 193,200.00 USD annually
DESCRIPTION
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.
LOCATION
USA, CA, Sunnyvale - 171,600.00 - 222,200.00 USD annually
USA, WA, Bellevue - 142,800.00 - 193,200.00 USD annually
See all 339+ OPT Applied AI Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new OPT Applied AI Engineer Jobs.
Get Access To All JobsTips for Finding OPT Sponsorship as an Applied AI Engineer
Target companies with active AI infrastructure teams
Companies building internal AI platforms, not just integrating third-party APIs, are far more likely to sponsor. Look for job postings that mention model fine-tuning, inference optimization, or LLM deployment pipelines as indicators of serious AI engineering investment.
Lead with production deployment experience
Employers distinguish Applied AI Engineers from researchers by deployment track record. Highlight any experience shipping models to production, managing latency constraints, or building evaluation frameworks rather than leading with academic research or Jupyter notebook projects.
Understand your STEM OPT timeline before applying
Standard OPT gives you 12 months. A STEM extension adds 24 more, totaling 36 months. Computer science and related degrees almost always qualify. Knowing your exact end dates helps you time applications and negotiate start dates with confidence.
Frame your H-1B sponsorship ask as a process, not a favor
Many employers are unfamiliar with OPT-to-H-1B visa timelines. Explain that STEM OPT covers you through multiple H-1B lottery cycles, reducing their urgency. Framing it as a straightforward process with a clear runway removes the perception of risk.
Build a portfolio around measurable AI system outcomes
Applied AI roles favor engineers who can show business impact. Document projects with specific metrics: latency reductions, accuracy improvements, cost-per-inference figures, or user adoption numbers. Concrete outcomes signal readiness for production engineering, not just prototyping.
Browse OPT-friendly Applied AI Engineer roles on Migrate Mate
Migrate Mate surfaces Applied AI Engineer jobs from employers with a demonstrated history of sponsoring F-1 OPT candidates. Filtering by sponsorship history before applying saves time and increases the likelihood your application lands with a visa-ready employer.
Applied AI Engineer OPT: Frequently Asked Questions
Can I work as an Applied AI Engineer on F-1 OPT without an H-1B?
Yes. F-1 OPT authorizes full-time employment in roles directly related to your degree, and Applied AI Engineering almost always qualifies under computer science, electrical engineering, or a related STEM field. Standard OPT covers 12 months. If your degree is STEM-designated, you can apply for a 24-month extension, giving you up to 36 months of work authorization before needing H-1B or another visa.
Does an Applied AI Engineer role qualify for the STEM OPT extension?
In nearly every case, yes. Applied AI Engineering requires skills in machine learning, software systems, or data science, all of which map directly to CIP codes that qualify for STEM OPT. Your employer must also be enrolled in E-Verify, which most companies of meaningful size already satisfy. Confirm your specific degree's CIP code with your DSO to be certain before applying for the extension.
How many H-1B lottery cycles does my OPT cover as an Applied AI Engineer?
With a STEM OPT extension, you have 36 months of work authorization. The H-1B lottery runs each spring for an October 1 start date. Depending on when your OPT begins, you may be eligible for two or three lottery cycles before your authorization expires. Applied AI Engineers are among the most commonly sponsored H-1B roles, so employers in this space are generally familiar with the timeline.
What should I look for in an employer to ensure they'll sponsor my visa?
Look for employers who have previously filed H-1B petitions, are enrolled in E-Verify, and have dedicated HR or legal teams familiar with immigration processes. Larger tech companies, AI-focused startups with institutional funding, and enterprises with established engineering departments are the most reliable sponsors. Migrate Mate filters Applied AI Engineer jobs by sponsorship history so you can focus your search on employers already set up to support OPT candidates.
Does my job function need to match my degree exactly for OPT to be valid?
Not exactly, but the role must be directly related to your degree field. An Applied AI Engineer role requiring knowledge of machine learning, statistical modeling, or software engineering aligns with degrees in computer science, electrical engineering, applied mathematics, and similar disciplines. Your DSO makes the final determination, but most Applied AI roles are well within the acceptable scope for STEM degree holders on OPT.