H-1B Visa Applied Scientist Jobs
Applied Scientist roles sit squarely within H-1B visa specialty occupation requirements, with most positions requiring a master's or PhD in computer science, statistics, or a related field. Large tech and research employers file H-1B petitions regularly for this title, making it one of the more sponsorship-accessible roles in data and machine learning.
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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 1,261+ H-1B Visa Applied Scientist Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B Visa Applied Scientist Jobs.
Get Access To All JobsTips for Finding H-1B Visa Sponsorship as an Applied Scientist
Align your degree to the SOC code
USCIS evaluates specialty occupation by matching your degree field to the role's SOC code. Applied Scientist roles often file under 15-2051 (Data Scientists) or 15-1221 (Computer and Information Research Scientists), so confirm your transcript language maps directly to whichever code your offer letter uses.
Check LCA filings before applying
Use Migrate Mate to filter Applied Scientist roles by employers with active H-1B LCA filing history. This surfaces companies that have already cleared DOL prevailing-wage certification for this title, cutting out employers who've never navigated the process.
Verify prevailing wage before negotiating
Run your target job's SOC code and work location through the OFLC Wage Search before your offer negotiation. Your employer's LCA must certify a wage at or above the DOL prevailing wage, so knowing the Level I through Level IV thresholds gives you a concrete floor to reference.
Request a cap-exempt employer when possible
Universities, nonprofit research organizations, and government research entities can file H-1B petitions outside the annual cap and lottery. Applied Scientist roles at national labs or research hospitals are often cap-exempt, which means you can start work on approval rather than waiting for an October 1 start date.
Prepare a strong body-of-work portfolio early
USCIS officers occasionally issue RFEs on Applied Scientist petitions questioning whether the role genuinely requires a specific degree. Published papers, patents, or documented model deployments tied to your academic specialty strengthen the specialty occupation argument before an RFE ever arrives.
Clarify filing responsibility before signing
Confirm in writing whether your employer uses in-house legal counsel or an outside immigration firm for H-1B filings, and ask who bears the I-129 filing fee. Employers are prohibited from passing certain USCIS fees to H-1B workers, so get the fee structure documented before your start date.
H-1B Visa Applied Scientist: Frequently Asked Questions
Does an Applied Scientist role qualify as a specialty occupation for H-1B purposes?
Yes. Applied Scientist positions typically require at least a bachelor's degree, and most employers require a master's or PhD in computer science, statistics, mathematics, or a closely related field. USCIS evaluates the specific degree requirement stated in the job description, so the offer letter and job duties need to clearly tie the role to a particular academic discipline rather than accepting any bachelor's degree.
Which employers sponsor H-1B visas for Applied Scientist roles?
Technology companies, cloud platform providers, financial services firms, and research-focused healthcare organizations are among the most active H-1B sponsors for Applied Scientist titles. You can search Migrate Mate to filter Applied Scientist openings by employers with verified H-1B LCA filing history, so you're targeting companies that have already demonstrated they sponsor this specific role type.
How does the H-1B lottery affect my timeline as an Applied Scientist?
If your target employer is cap-subject, USCIS opens H-1B registration in March and selects registrations by lottery. Selected petitions can begin October 1 of that fiscal year. Cap-exempt employers, including universities and qualifying nonprofit research organizations, can file at any time, which is worth factoring in when you're evaluating Applied Scientist opportunities.
What SOC code is typically used for Applied Scientist H-1B filings?
Most Applied Scientist petitions file under 15-2051 (Data Scientists) or 15-1221 (Computer and Information Research Scientists), depending on whether the role is primarily modeling and analysis or foundational research. The SOC code determines the DOL prevailing wage level your employer must certify on the LCA, so it directly affects your minimum compensation floor. You can cross-reference the O*NET profile for each code to see which best matches your actual duties.
Can I transfer my H-1B to a new Applied Scientist role at a different employer?
Yes. H-1B portability under AC21 lets you change employers once your petition has been pending for at least 180 days and you're moving to a same or similar occupational classification. Your new employer files an H-1B transfer petition, and you can start work as soon as USCIS receives it, without waiting for approval. Confirm with your new employer that the transfer petition will be filed before you leave your current role.