Green Card Principal Scientist Jobs
Principal Scientist roles at biotech, pharmaceutical, and research organizations routinely qualify for EB-2 and EB-3 green card sponsorship through the PERM labor certification process. Employers must document that no qualified U.S. worker is available before filing your I-140 petition, making your specialized research credentials and publication record central to a successful case.
Find Green Card Principal Scientist JobsOverview
Showing 5 of 791+ Principal Scientist jobs
See all 791+ Principal Scientist Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Principal Scientist roles.
Get Access To All JobsOverview
Intuit Customer Success is building the knowledge and agentic intelligence foundation that will power how millions of customers get help and how our teams make decisions based on knowledge and data. We’re looking for a hands-on Principal Knowledge Engineer to be the architect of that data and knowledge foundation.
You will define and own the knowledge architecture and governance for the ICS data platform and agentic intelligence layer, including the Intuit Customer Success Knowledge Graph. Your work will power use cases such as internal customer success agent experiences and self-serve data agents that let teams ask questions in plain language and get trusted answers. This is a rare chance to set the architecture and system for an enterprise-scale agentic intelligence platform from the ground up. You will guide a team of data scientists, data professionals, and engineers, and you will stay close enough to the code to build, prototype and unblock.
Responsibilities
- Own the agentic knowledge architecture. Define the target architecture for how ICS knowledge is modeled, stored, connected and served to agents and people. This covers the Intuit Customer Success Knowledge Graph, ontologies and taxonomies, entity resolution, and how structured data, unstructured content and expert knowledge come together.
- Architect the agentic intelligence layer. Set the patterns for how agents retrieve, reason over and act on knowledge. This includes GraphRAG and hybrid retrieval (graph, vector and structured queries), context engineering, tool and MCP design, multi-agent orchestration, and memory.
- Set the semantic layer strategy. Define how business metrics, entities and definitions are expressed once and reused across dashboards, data agents and AI experiences. The goal is that “what does this number mean” has one answer everywhere.
- Lead data and knowledge governance. Build the governance strategy for knowledge quality, freshness, lineage, provenance, access control and responsible AI. Make sure agents answer from trusted, compliant and well-attributed sources.
- Make the key architecture and tooling decisions. Evaluate and select graph stores, vector stores, semantic layer tools, agent frameworks and evaluation tooling. Make clear build-versus-buy calls and document the tradeoffs.
- Build hands-on. Prototype reference implementations, write production-quality code, review designs and pull requests, and step into the hardest technical problems yourself.
- Balance now and next. Deliver what the business needs this quarter, such as tax-season readiness and high-impact self-serve use cases. Make sure each delivery moves toward the long-term architecture instead of adding technical debt.
- Define how we measure quality. Set the evaluation frameworks for knowledge coverage, retrieval quality, answer accuracy, groundedness and agent reliability, and use them to drive prioritization.
- Translate between business and technology. Work with segment data leaders, business, engineering, and operations to understand their workflows, nuances and decisions. Explain architectural choices in business terms and build alignment across teams.
- Guide and grow the team. Mentor data scientists, technical data analysts, and engineers. Raise the technical bar through design reviews and standards, and build a strong knowledge engineering practice across ICS.
Qualifications
Required
- 15+ years in data and knowledge engineering, or AI and ML engineering, including significant time as a technical lead or architect on large-scale data or knowledge platforms.
- A track record of architecting and shipping large-scale data and knowledge systems in production, such as enterprise knowledge graphs, semantic platforms, or data platforms that serve many teams and use cases.
- Deep expertise in knowledge modeling, including ontology and taxonomy design, entity resolution, graph data modeling and knowledge graph construction. This includes using LLMs for extraction and enrichment.
- Hands-on experience building LLM-powered and agentic systems, including RAG and GraphRAG, hybrid retrieval, agent frameworks (for example LangGraph, LlamaIndex, MCP), context engineering and agent evaluation.
- Deep knowledge of AI infrastructure: vector and graph databases (for example Neo4j, Neptune), embedding and retrieval pipelines, model serving, cloud data platforms (for example Databricks, Snowflake, AWS), and their scale, latency and cost tradeoffs.
- Experience defining semantic layer and metrics strategy so that business definitions stay consistent across BI and AI consumers.
- Experience designing data and knowledge governance, including lineage, provenance, quality, freshness, access control and responsible AI practices.
- Strong programming skills and the ability to write and review production code.
- A pragmatic approach to architecture: you can meet short-term business needs with flexible solutions while steadily building toward a long-term vision.
- Excellent communication and the ability to influence without authority. You understand people, process and business needs, and you can speak the business team’s language as fluently as the engineering team’s.
- Experience mentoring and technically guiding teams of data scientists, data professionals and engineers.
- BS in Computer Science, Data Science, Statistics, Information Science or a related field. An MS or PhD is preferred.
Preferred
- Experience with knowledge systems for customer success, customer support or contact-center environments, such as agent assist, knowledge-base optimization or self-service.
- Experience building natural-language-to-SQL or self-serve data agents over enterprise warehouses.
- Familiarity with semantic web standards (RDF, OWL, SHACL, SPARQL) alongside property graphs (Cypher).
- Experience with LLM evaluation and observability tooling (for example Langfuse, MLflow) and evaluation-driven development.
- Experience in regulated domains such as finance or tax, where accuracy, provenance and compliance are critical.
- Publications, patents, open-source contributions or conference talks in knowledge graphs, semantic AI or agentic systems.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
The expected base pay range for this position is:
Mountain View $247,500 - $335,000
Overview
Intuit Customer Success is building the knowledge and agentic intelligence foundation that will power how millions of customers get help and how our teams make decisions based on knowledge and data. We’re looking for a hands-on Principal Knowledge Engineer to be the architect of that data and knowledge foundation.
You will define and own the knowledge architecture and governance for the ICS data platform and agentic intelligence layer, including the Intuit Customer Success Knowledge Graph. Your work will power use cases such as internal customer success agent experiences and self-serve data agents that let teams ask questions in plain language and get trusted answers. This is a rare chance to set the architecture and system for an enterprise-scale agentic intelligence platform from the ground up. You will guide a team of data scientists, data professionals, and engineers, and you will stay close enough to the code to build, prototype and unblock.
Responsibilities
- Own the agentic knowledge architecture. Define the target architecture for how ICS knowledge is modeled, stored, connected and served to agents and people. This covers the Intuit Customer Success Knowledge Graph, ontologies and taxonomies, entity resolution, and how structured data, unstructured content and expert knowledge come together.
- Architect the agentic intelligence layer. Set the patterns for how agents retrieve, reason over and act on knowledge. This includes GraphRAG and hybrid retrieval (graph, vector and structured queries), context engineering, tool and MCP design, multi-agent orchestration, and memory.
- Set the semantic layer strategy. Define how business metrics, entities and definitions are expressed once and reused across dashboards, data agents and AI experiences. The goal is that “what does this number mean” has one answer everywhere.
- Lead data and knowledge governance. Build the governance strategy for knowledge quality, freshness, lineage, provenance, access control and responsible AI. Make sure agents answer from trusted, compliant and well-attributed sources.
- Make the key architecture and tooling decisions. Evaluate and select graph stores, vector stores, semantic layer tools, agent frameworks and evaluation tooling. Make clear build-versus-buy calls and document the tradeoffs.
- Build hands-on. Prototype reference implementations, write production-quality code, review designs and pull requests, and step into the hardest technical problems yourself.
- Balance now and next. Deliver what the business needs this quarter, such as tax-season readiness and high-impact self-serve use cases. Make sure each delivery moves toward the long-term architecture instead of adding technical debt.
- Define how we measure quality. Set the evaluation frameworks for knowledge coverage, retrieval quality, answer accuracy, groundedness and agent reliability, and use them to drive prioritization.
- Translate between business and technology. Work with segment data leaders, business, engineering, and operations to understand their workflows, nuances and decisions. Explain architectural choices in business terms and build alignment across teams.
- Guide and grow the team. Mentor data scientists, technical data analysts, and engineers. Raise the technical bar through design reviews and standards, and build a strong knowledge engineering practice across ICS.
Qualifications
Required
- 15+ years in data and knowledge engineering, or AI and ML engineering, including significant time as a technical lead or architect on large-scale data or knowledge platforms.
- A track record of architecting and shipping large-scale data and knowledge systems in production, such as enterprise knowledge graphs, semantic platforms, or data platforms that serve many teams and use cases.
- Deep expertise in knowledge modeling, including ontology and taxonomy design, entity resolution, graph data modeling and knowledge graph construction. This includes using LLMs for extraction and enrichment.
- Hands-on experience building LLM-powered and agentic systems, including RAG and GraphRAG, hybrid retrieval, agent frameworks (for example LangGraph, LlamaIndex, MCP), context engineering and agent evaluation.
- Deep knowledge of AI infrastructure: vector and graph databases (for example Neo4j, Neptune), embedding and retrieval pipelines, model serving, cloud data platforms (for example Databricks, Snowflake, AWS), and their scale, latency and cost tradeoffs.
- Experience defining semantic layer and metrics strategy so that business definitions stay consistent across BI and AI consumers.
- Experience designing data and knowledge governance, including lineage, provenance, quality, freshness, access control and responsible AI practices.
- Strong programming skills and the ability to write and review production code.
- A pragmatic approach to architecture: you can meet short-term business needs with flexible solutions while steadily building toward a long-term vision.
- Excellent communication and the ability to influence without authority. You understand people, process and business needs, and you can speak the business team’s language as fluently as the engineering team’s.
- Experience mentoring and technically guiding teams of data scientists, data professionals and engineers.
- BS in Computer Science, Data Science, Statistics, Information Science or a related field. An MS or PhD is preferred.
Preferred
- Experience with knowledge systems for customer success, customer support or contact-center environments, such as agent assist, knowledge-base optimization or self-service.
- Experience building natural-language-to-SQL or self-serve data agents over enterprise warehouses.
- Familiarity with semantic web standards (RDF, OWL, SHACL, SPARQL) alongside property graphs (Cypher).
- Experience with LLM evaluation and observability tooling (for example Langfuse, MLflow) and evaluation-driven development.
- Experience in regulated domains such as finance or tax, where accuracy, provenance and compliance are critical.
- Publications, patents, open-source contributions or conference talks in knowledge graphs, semantic AI or agentic systems.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
The expected base pay range for this position is:
Mountain View $247,500 - $335,000
See all 791+ Green Card Principal Scientist Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Green Card Principal Scientist Jobs.
Get Access To All JobsTips for Finding Green Card Sponsorship as a Principal Scientist
Document your research contributions before applying
Compile peer-reviewed publications, patents, conference presentations, and grant awards into a single credentials portfolio. PERM audits for Principal Scientist roles frequently probe whether your specialized expertise is genuinely distinct from the general U.S. labor market.
Target employers with established PERM filing histories
Large pharmaceutical, biotech, and contract research organizations file PERM applications regularly and have internal immigration teams. Prioritize companies where sponsorship is a standard hiring practice, not a one-off exception you'd need to negotiate from scratch.
Clarify EB-2 versus EB-3 eligibility with your hiring manager
Principal Scientist roles typically require a Ph.D. or master's degree, which may support EB-2 classification. Whether your employer files EB-2 or EB-3 affects your priority date and wait time, especially if you're from India or China.
Use Migrate Mate to filter sponsoring employers by role
Search Principal Scientist positions filtered by green card sponsorship history on Migrate Mate. You'll see which employers have active PERM filings for research roles, saving weeks of manual outreach to companies that don't sponsor.
Negotiate job description language before the PERM is filed
The PERM labor certification locks in the minimum requirements for your role. If the posted job description overstates requirements or uses vague language, DOL may reject the application. Review the draft with your employer's immigration counsel before submission.
Understand how PERM timing affects your employment start date
PERM processing at DOL currently runs six to twelve months before your employer can file the I-140 petition. Factor this into your start-date negotiations so your work authorization doesn't lapse while the case moves through USCIS.
Green Card Principal Scientist: Frequently Asked Questions
Does a Principal Scientist role qualify for EB-2 or EB-3 green card sponsorship?
Most Principal Scientist positions qualify for EB-2 because they require an advanced degree, typically a master's or Ph.D. in a life science, chemistry, or engineering discipline. If the employer's minimum requirement is a bachelor's degree with several years of experience, the role may be classified as EB-3 instead. Your employer's immigration counsel determines the category based on the actual job requirements documented in the PERM filing.
How is the PERM green card process different from H-1B sponsorship for Principal Scientists?
Unlike H-1B visa sponsorship, PERM leads to permanent residency rather than a temporary work visa, and there is no annual lottery. The tradeoff is time: PERM requires a DOL-supervised recruitment process to prove no qualified U.S. worker is available, followed by I-140 and adjustment of status filings. For Principal Scientists from most countries outside India and China, the entire process from PERM filing to green card approval can take two to four years.
Which employers sponsor Principal Scientists for green cards most often?
Pharmaceutical companies, biotech firms, academic medical centers, and government contractors are the most consistent sponsors for Principal Scientist green cards. These organizations have recurring research hiring needs and established relationships with immigration law firms. Use Migrate Mate to search Principal Scientist roles specifically filtered by green card sponsorship activity, which surfaces employers with documented PERM filing histories for research-level positions.
What role do prevailing wage determinations play in a Principal Scientist PERM case?
Before filing the PERM application, your employer must obtain a prevailing wage determination from OFLC using the OFLC Wage Search tool. The wage assigned reflects the complexity and specialization of your Principal Scientist role. If your offered salary falls below the prevailing wage level, DOL will not certify the PERM. You can cross-reference wage levels for your occupation using O*NET occupation codes and Bureau of Labor Statistics data.
Can my employer start the PERM process while I'm on an H-1B visa?
Yes, and starting early is strategically sound. Your employer can file the PERM while you're in H-1B status, and USCIS allows H-1B extensions beyond the standard six-year limit once your I-140 is approved and your priority date is not current. This means a Principal Scientist on H-1B can remain in valid status throughout a multi-year green card process without interruption, as long as the employer maintains the sponsorship.