Applied Scientist Jobs in USA with Visa Sponsorship
Applied Scientists combine advanced research with practical implementation, making them strong candidates for H-1B visa and O-1 visa sponsorship. The role typically requires a PhD in computer science, machine learning, statistics, or related field, which easily satisfies specialty occupation requirements. Major tech companies regularly sponsor Applied Scientists, with approval rates generally high due to clear degree-to-role alignment.
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Get Access To All JobsINTRODUCTION
The Work
Every legal matter is its own experiment. An attorney arrives with a theory of the case; the evidence arrives as hundreds of thousands of documents, sometimes millions, that no one has read and no model has seen. Somewhere in the cross product of the two are the answers that decide lawsuits, investigations, and livelihoods. Finding them quickly and defensibly, with the integrity and credibility attorneys can rely on, is the problem we own. We solve it creatively and rigorously.
Relativity is a data-centered, AI-native legal technology company, and Applied Science builds the AI inside Relativity aiR. We launched aiR in 2023 and have now run commercial generative AI in the legal domain for more than three years, powering work that includes the largest investigations in the world. Our systems are distinguished by the data they operate over (more than 93 petabytes) and the work they have done: over 190 million AI review decisions, backed by more than 1 billion generative sub-analyses in 2026 alone. The team is as distinctive as the data: legal experts, all former litigators, work directly inside Applied Science.
At Relativity, our mission is to Organize data. Discover the truth. Act on it. The Applied Science team serves this mission by building bold and ambitious AI systems. We are curious, dedicated, and humble. We understand complexity, uphold rigor, and measure relentlessly. We build and ship with pace. Above all, we are interdisciplinary collaborators and team players.
We're looking for a Staff Applied Scientist to take on our hardest problems in document vision and set standards that reach beyond a single team.
JOB DESCRIPTION AND REQUIREMENTS
Capable and Reliable
Two requests can look nearly identical and be worlds apart. "See if you can find me an example of this" needs a capable system: it finds the example or it doesn't. "Conduct a reasonable search for any and all documents responsive to this request" is a different kind of promise. Its answer spans a corpus no one will ever read end-to-end. So the system's process, as much as its output, has to earn the trust of the professionals who rely on it.
That property is reliability. It decomposes into consistency, robustness, calibration, and safety: systems that behave tomorrow the way they did today, degrade predictably under stress, know how confident they should be, and check their own work. Before aiR returns an analysis, it validates its citations and runs internal consistency checks; when a check fails, it refuses to answer. It has refused more than a million times so far in 2026, and we count every one as a success: an error caught before it reached a user.
You'll build for both, and help define the standard for how.
The Focus: Document Vision
This role anchors our document-vision work: teaching systems to read evidence the way legal professionals do. Real matters arrive as scanned pages, photographs, tables, handwriting, stamps, and broken layouts, at the scale of millions of documents. You'll own the science of multimodal document understanding across aiR, from vision-language modeling to the evaluation standards that make visual evidence usable and defensible.
- Take on the hardest, most ambiguous problems in the portfolio and produce clarity: a well-specified approach, an evaluation that settles the question, a system that ships.
- Set standards that reach beyond your team: evaluation methods, modeling patterns, and quality bars adopted by scientists you've never worked with.
- Own readiness for flagship AI systems, from problem framing through efficacy studies and production monitoring, in partnership with engineering.
- Multiply the team through deep review and mentorship of senior and lead scientists.
- Advise Applied Science leadership on where the science is going and where we should invest.
What You Bring
- A master's or PhD in computer science or another quantitative discipline (or equivalent professional experience), and at least 6 years of applied AI/ML experience.
- Years of production AI/ML behind you: systems you specified, shipped, and operated at scale, in partnership with the engineers who run them.
- Expert judgment about AI systems: you've built them, measured them, and formed views of their limits that hold up under challenge.
- Scientific rigor other people borrow: you supervise the data understanding of teams beyond your own, your evaluations become the template, and your error analyses end debates.
- Software-engineering judgment trusted across teams, and the programming skill to credibly prototype what you propose.
- An ownership mindset that extends across the organization.
Nice to Have
- Experience with vision-language or document-understanding models (layout analysis, OCR-adjacent pipelines, table and figure extraction) in production.
- An interest in legal technology and the justice system.
- Experience developing information retrieval systems.
- Experience developing agentic harnesses.
- Experience building reliable AI systems at scale.
Why Relativity Applied Science
This is the place where your curiosity, dedication, and talent will build products that power the pursuit of justice around the world.
Relativity is committed to competitive, fair, and equitable compensation practices.
This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.
The expected salary range for this role is between following values:
$197,000 and $295,000
The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.
Required Skills:
Algorithms, Computer Vision, Data Analysis, Data Science, Deep Learning, Machine Learning (ML), Natural Language, Natural Language Processing (NLP), Python (Programming Language), Scientific Research
INTRODUCTION
The Work
Every legal matter is its own experiment. An attorney arrives with a theory of the case; the evidence arrives as hundreds of thousands of documents, sometimes millions, that no one has read and no model has seen. Somewhere in the cross product of the two are the answers that decide lawsuits, investigations, and livelihoods. Finding them quickly and defensibly, with the integrity and credibility attorneys can rely on, is the problem we own. We solve it creatively and rigorously.
Relativity is a data-centered, AI-native legal technology company, and Applied Science builds the AI inside Relativity aiR. We launched aiR in 2023 and have now run commercial generative AI in the legal domain for more than three years, powering work that includes the largest investigations in the world. Our systems are distinguished by the data they operate over (more than 93 petabytes) and the work they have done: over 190 million AI review decisions, backed by more than 1 billion generative sub-analyses in 2026 alone. The team is as distinctive as the data: legal experts, all former litigators, work directly inside Applied Science.
At Relativity, our mission is to Organize data. Discover the truth. Act on it. The Applied Science team serves this mission by building bold and ambitious AI systems. We are curious, dedicated, and humble. We understand complexity, uphold rigor, and measure relentlessly. We build and ship with pace. Above all, we are interdisciplinary collaborators and team players.
We're looking for a Staff Applied Scientist to take on our hardest problems in document vision and set standards that reach beyond a single team.
JOB DESCRIPTION AND REQUIREMENTS
Capable and Reliable
Two requests can look nearly identical and be worlds apart. "See if you can find me an example of this" needs a capable system: it finds the example or it doesn't. "Conduct a reasonable search for any and all documents responsive to this request" is a different kind of promise. Its answer spans a corpus no one will ever read end-to-end. So the system's process, as much as its output, has to earn the trust of the professionals who rely on it.
That property is reliability. It decomposes into consistency, robustness, calibration, and safety: systems that behave tomorrow the way they did today, degrade predictably under stress, know how confident they should be, and check their own work. Before aiR returns an analysis, it validates its citations and runs internal consistency checks; when a check fails, it refuses to answer. It has refused more than a million times so far in 2026, and we count every one as a success: an error caught before it reached a user.
You'll build for both, and help define the standard for how.
The Focus: Document Vision
This role anchors our document-vision work: teaching systems to read evidence the way legal professionals do. Real matters arrive as scanned pages, photographs, tables, handwriting, stamps, and broken layouts, at the scale of millions of documents. You'll own the science of multimodal document understanding across aiR, from vision-language modeling to the evaluation standards that make visual evidence usable and defensible.
- Take on the hardest, most ambiguous problems in the portfolio and produce clarity: a well-specified approach, an evaluation that settles the question, a system that ships.
- Set standards that reach beyond your team: evaluation methods, modeling patterns, and quality bars adopted by scientists you've never worked with.
- Own readiness for flagship AI systems, from problem framing through efficacy studies and production monitoring, in partnership with engineering.
- Multiply the team through deep review and mentorship of senior and lead scientists.
- Advise Applied Science leadership on where the science is going and where we should invest.
What You Bring
- A master's or PhD in computer science or another quantitative discipline (or equivalent professional experience), and at least 6 years of applied AI/ML experience.
- Years of production AI/ML behind you: systems you specified, shipped, and operated at scale, in partnership with the engineers who run them.
- Expert judgment about AI systems: you've built them, measured them, and formed views of their limits that hold up under challenge.
- Scientific rigor other people borrow: you supervise the data understanding of teams beyond your own, your evaluations become the template, and your error analyses end debates.
- Software-engineering judgment trusted across teams, and the programming skill to credibly prototype what you propose.
- An ownership mindset that extends across the organization.
Nice to Have
- Experience with vision-language or document-understanding models (layout analysis, OCR-adjacent pipelines, table and figure extraction) in production.
- An interest in legal technology and the justice system.
- Experience developing information retrieval systems.
- Experience developing agentic harnesses.
- Experience building reliable AI systems at scale.
Why Relativity Applied Science
This is the place where your curiosity, dedication, and talent will build products that power the pursuit of justice around the world.
Relativity is committed to competitive, fair, and equitable compensation practices.
This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.
The expected salary range for this role is between following values:
$197,000 and $295,000
The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.
Required Skills:
Algorithms, Computer Vision, Data Analysis, Data Science, Deep Learning, Machine Learning (ML), Natural Language, Natural Language Processing (NLP), Python (Programming Language), Scientific Research
See all 1,382+ Applied Scientist Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Applied Scientist roles.
Get Access To All JobsTips for Finding Visa Sponsorship as an Applied Scientist
Emphasize your PhD and research publications
Applied Scientist roles strongly favor PhD holders. Highlight your dissertation research, peer-reviewed publications, and conference presentations to demonstrate the advanced knowledge that justifies H-1B specialty occupation requirements.
Target companies with established ML research divisions
Amazon, Google, Microsoft, and Meta regularly sponsor Applied Scientists. These companies understand the role's requirements and have streamlined visa processes for research-focused positions with clear business applications.
Document your track record of research-to-product translation
Applied Scientists bridge research and implementation. Showcase projects where your research led to deployed systems, improved algorithms, or measurable business impact to strengthen your visa petition.
Consider the O-1 visa if you have exceptional research credentials
If you have significant publications, citations, conference awards, or recognition in AI/ML research, the O-1 extraordinary ability visa offers advantages over H-1B including no lottery and faster processing.
Prepare for technical interviews that demonstrate specialized knowledge
Visa interviews may include questions about your research methodology and how it applies to the role. Be ready to explain complex algorithms and statistical methods in accessible terms.
Highlight interdisciplinary expertise in your petition
Applied Scientists often work across domains like computer vision, NLP, or robotics. Document how your cross-disciplinary knowledge creates unique value that requires specialized H-1B talent rather than general programmers.
Frequently Asked Questions
What degree do I need for Applied Scientist H-1B sponsorship?
Most Applied Scientist positions require a PhD in computer science, machine learning, statistics, mathematics, or closely related field. Some roles accept a Master's with extensive research experience, but PhD holders have significantly stronger H-1B visa petitions due to the advanced specialization required for research-to-product development.
Which companies sponsor Applied Scientists for visas?
Major tech companies including Amazon, Google, Microsoft, Meta, Apple, and Netflix regularly sponsor Applied Scientists. Research-focused startups and companies in autonomous vehicles, healthcare AI, and fintech also sponsor, though they may have less experience with the visa process than established tech giants.
How do Applied Scientist H-1B approval rates compare to other roles?
Applied Scientist positions generally have high H-1B approval rates due to clear specialty occupation requirements. The PhD requirement and specialized research knowledge make it easy to demonstrate that the role requires theoretical and practical application of highly specialized knowledge in computer science or mathematics.
How to find Applied Scientist jobs with visa sponsorship?
To find Applied Scientist positions with visa sponsorship, use Migrate Mate, which specializes in connecting international talent with sponsoring employers. Focus on tech companies, research institutions, and AI startups that commonly hire Applied Scientists on H-1B, O-1 visa, or EB-2 visas. These roles are in high demand at companies developing machine learning, data science, and research products.
Can I get sponsored as an Applied Scientist with only industry experience?
While possible, it's challenging without advanced degrees. USCIS typically expects Applied Scientists to have PhD-level research training. If you only have industry experience, you'd need to demonstrate extraordinary depth in machine learning research, publications, or patents that substitute for formal academic credentials.
Should I apply for O-1 instead of H-1B as an Applied Scientist?
Consider O-1 if you have exceptional research credentials like high-impact publications, significant citations, conference awards, or recognition as a leading researcher in AI/ML. O-1 has no lottery, faster processing, and allows more flexibility, but requires demonstrating extraordinary ability rather than just specialty occupation requirements.
What is the prevailing wage requirement for sponsored Applied Scientist jobs?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.