H-1B Visa Research Product Manager Jobs
Research Product Manager roles sit at the intersection of user research and product strategy, making them strong candidates for H-1B specialty occupation status. Employers filing your H-1B must certify a prevailing wage via a Labor Condition Application before USCIS can approve your petition. Cap-subject registrations open each April for an October 1 start date.
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Get Access To All JobsResearch Product Manager — AI Systems (Structured Data, Evaluation & Learning Efficiency)
About the Role
We’re hiring a Research Product Manager to define and build core systems that determine how AI models are evaluated, improved, and deployed on real-world data.
You’ll work on systems spanning:
- model evaluation and benchmarking
- post-training and feedback loops
- structured and relational data learning
- performance, efficiency, and cost optimization
This role sits at the intersection of ML infrastructure, research, and product. It is closest to roles like ML platform PM or AI infrastructure PM, but with deeper ownership of how systems are designed and how model performance translates into real-world outcomes.
You’ll partner closely with researchers and engineers to move ideas from experiments into production systems used at scale.
The Mission
AI today is no longer bottlenecked by model architecture alone.
The real constraints are:
- how models are evaluated
- how they improve after training
- how they behave in real-world systems
Granica is building the systems that solve this.
We are a research and systems company led by Prof. Andrea Montanari (Stanford), focused on:
- evaluation as a first-class system
- post-training as a continuous learning loop
- efficient learning over real-world data
Most real-world data is structured and relational, yet modern AI systems remain poorly optimized to learn from it.
Our thesis:
AI advantage will come from how efficiently models learn from structured data—and how that translates into economic value.
What You’ll Do
- Define and drive systems for model evaluation, benchmarking, and real-world performance
- Build product direction for post-training systems and feedback loops that continuously improve models
- Define how models learn from large-scale structured and relational datasets
- Partner with engineering to build systems that connect data platforms (warehouses, lakehouses) with ML systems
- Own how improvements move from research experiments into production systems
- Model trade-offs across compute, data efficiency, performance, and cost
- Identify where system improvements drive measurable business impact
Skills and Qualifications
Minimum Qualifications
- 5+ years of experience in product management, technical program management, or similar roles in AI, ML infrastructure, or data systems
- Strong understanding of machine learning systems, including training, evaluation, and deployment
- Experience working with large-scale data systems or distributed infrastructure
- Ability to reason about trade-offs across data, compute, performance, and cost
- Track record of driving complex technical systems from concept to production
Preferred Qualifications
- Experience with ML platforms, LLM systems, or AI infrastructure
- Experience with evaluation systems, observability, or model performance tooling
- Familiarity with structured or relational data systems (e.g., warehouses, lakehouses)
- Background in engineering, applied research, or ML systems development
- Experience operating in research-driven or highly ambiguous environments
Ideal Backgrounds
- ML / AI infrastructure PMs (OpenAI, Google, Meta, Snowflake, Databricks, AWS, or similar)
- Product leaders in model systems, evaluation, or observability
- Research engineers or applied scientists transitioning into product
- Engineers who have built ML or data systems and taken on product ownership
Why This Role Matters
Most AI systems are limited not by model capability, but by:
- weak evaluation systems
- inefficient learning loops
- poor utilization of structured data
- lack of connection between performance and real-world outcomes
This role defines how those constraints are solved in production systems. You won’t be optimizing features—you’ll be defining the systems that determine how models improve, how they are trusted, and how they deliver value.
Logistics
- Location: Mountain View, CA
- Work model: On-site, five days per week
- Level: Senior / Staff / Principal (depending on experience)
Compensation & Benefits
- Competitive salary, meaningful equity, and substantial bonus for top performers
- Flexible time off plus comprehensive health coverage for you and your family
- Support for research, publication, and deep technical exploration
At Granica, you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring. Join us to build the foundational data systems that power the future of enterprise AI!
Research Product Manager — AI Systems (Structured Data, Evaluation & Learning Efficiency)
About the Role
We’re hiring a Research Product Manager to define and build core systems that determine how AI models are evaluated, improved, and deployed on real-world data.
You’ll work on systems spanning:
- model evaluation and benchmarking
- post-training and feedback loops
- structured and relational data learning
- performance, efficiency, and cost optimization
This role sits at the intersection of ML infrastructure, research, and product. It is closest to roles like ML platform PM or AI infrastructure PM, but with deeper ownership of how systems are designed and how model performance translates into real-world outcomes.
You’ll partner closely with researchers and engineers to move ideas from experiments into production systems used at scale.
The Mission
AI today is no longer bottlenecked by model architecture alone.
The real constraints are:
- how models are evaluated
- how they improve after training
- how they behave in real-world systems
Granica is building the systems that solve this.
We are a research and systems company led by Prof. Andrea Montanari (Stanford), focused on:
- evaluation as a first-class system
- post-training as a continuous learning loop
- efficient learning over real-world data
Most real-world data is structured and relational, yet modern AI systems remain poorly optimized to learn from it.
Our thesis:
AI advantage will come from how efficiently models learn from structured data—and how that translates into economic value.
What You’ll Do
- Define and drive systems for model evaluation, benchmarking, and real-world performance
- Build product direction for post-training systems and feedback loops that continuously improve models
- Define how models learn from large-scale structured and relational datasets
- Partner with engineering to build systems that connect data platforms (warehouses, lakehouses) with ML systems
- Own how improvements move from research experiments into production systems
- Model trade-offs across compute, data efficiency, performance, and cost
- Identify where system improvements drive measurable business impact
Skills and Qualifications
Minimum Qualifications
- 5+ years of experience in product management, technical program management, or similar roles in AI, ML infrastructure, or data systems
- Strong understanding of machine learning systems, including training, evaluation, and deployment
- Experience working with large-scale data systems or distributed infrastructure
- Ability to reason about trade-offs across data, compute, performance, and cost
- Track record of driving complex technical systems from concept to production
Preferred Qualifications
- Experience with ML platforms, LLM systems, or AI infrastructure
- Experience with evaluation systems, observability, or model performance tooling
- Familiarity with structured or relational data systems (e.g., warehouses, lakehouses)
- Background in engineering, applied research, or ML systems development
- Experience operating in research-driven or highly ambiguous environments
Ideal Backgrounds
- ML / AI infrastructure PMs (OpenAI, Google, Meta, Snowflake, Databricks, AWS, or similar)
- Product leaders in model systems, evaluation, or observability
- Research engineers or applied scientists transitioning into product
- Engineers who have built ML or data systems and taken on product ownership
Why This Role Matters
Most AI systems are limited not by model capability, but by:
- weak evaluation systems
- inefficient learning loops
- poor utilization of structured data
- lack of connection between performance and real-world outcomes
This role defines how those constraints are solved in production systems. You won’t be optimizing features—you’ll be defining the systems that determine how models improve, how they are trusted, and how they deliver value.
Logistics
- Location: Mountain View, CA
- Work model: On-site, five days per week
- Level: Senior / Staff / Principal (depending on experience)
Compensation & Benefits
- Competitive salary, meaningful equity, and substantial bonus for top performers
- Flexible time off plus comprehensive health coverage for you and your family
- Support for research, publication, and deep technical exploration
At Granica, you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring. Join us to build the foundational data systems that power the future of enterprise AI!
See all 113+ Research Product Manager jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Research Product Manager roles.
Get Access To All JobsTips for Finding H-1B Visa Sponsorship as a Research Product Manager
Document your specialty occupation evidence early
Research Product Managers sometimes face RFEs because the role spans research and product. Gather job descriptions, org charts, and degree transcripts that tie your specific research methodology expertise to a bachelor's-level field requirement before applying.
Target cap-exempt employers for faster timelines
Universities, nonprofit research institutions, and affiliated entities are cap-exempt, meaning your H-1B can start any month without waiting for October 1. These employers also frequently hire Research PMs to lead user and market research programs.
Search verified H-1B sponsors on Migrate Mate
Filter by Research Product Manager roles on Migrate Mate to see employers with active H-1B LCA filing history, so you apply to companies already familiar with the sponsorship process rather than educating a first-time sponsor.
Cross-check prevailing wage before accepting offers
Use the OFLC Wage Search to look up the Level II or Level III wage for your SOC code and work location. An offer below the prevailing wage means DOL won't certify the LCA, which blocks the entire H-1B filing.
Clarify who owns the O*NET classification internally
Ask HR which SOC code they plan to use on the LCA before the offer letter is signed. Research Product Manager duties can map to multiple O*NET codes, and the wrong classification can mismatch your degree field and trigger a USCIS specialty occupation denial.
Negotiate your start date around the cap-gap window
If you're on OPT when your H-1B is selected, the cap-gap rule automatically extends your work authorization through September 30. Structure your offer letter to reflect this so you don't have an unauthorized gap between OPT expiry and your H-1B start date.
Research Product Manager jobs are hiring across the US. Find yours.
Find Research Product Manager JobsResearch Product Manager H-1B Visa: Frequently Asked Questions
Does a Research Product Manager role qualify as an H-1B specialty occupation?
Yes, but the employer needs to demonstrate the role normally requires at least a bachelor's degree in a specific field such as human-computer interaction, cognitive science, market research, or a closely related discipline. Roles that accept any degree may face an RFE from USCIS. Your job description should explicitly state the degree requirement and connect research methodology skills to the academic field.
How do I find employers who actively sponsor H-1B visas for Research Product Managers?
Migrate Mate lets you filter Research Product Manager roles by employers with verified H-1B LCA filing history, so you can prioritize companies that have already navigated the sponsorship process. This is more reliable than assuming any company that lists the title is willing to sponsor, since sponsorship willingness varies significantly even among large tech and research-focused employers.
Can a startup sponsor my H-1B for a Research Product Manager position?
Yes, startups can sponsor H-1B visas regardless of size, but USCIS scrutinizes smaller employers more closely on financial ability to pay the prevailing wage and on whether the specialty occupation standard is met. You'll want the offer letter to clearly define your research-specific duties, and the employer should be prepared to provide financial documentation if requested during adjudication.
What happens to my H-1B status if the research program I manage is discontinued?
If your employer terminates your position, you have a 60-day grace period to find a new H-1B sponsor, change to another valid status, or depart the U.S. Your new employer would need to file an H-1B transfer petition before the grace period ends. Research PM roles can sometimes be reclassified or moved to a different cost center, so confirm with HR whether a departmental change triggers a new LCA requirement.
Does my research-focused master's degree strengthen my H-1B petition as a Research Product Manager?
A graduate degree in a research-relevant field such as behavioral science, UX research, or applied psychology directly supports the specialty occupation argument and can place you at a higher prevailing wage level on the OFLC Wage Search. USCIS looks for a direct connection between the degree field and the job duties, so a research methods-heavy graduate program is more useful than a general MBA for this specific role.
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