H-1B Visa Machine Learning Manager Jobs
Machine Learning Manager roles qualify as H-1B visa specialty occupations under the computer and information systems manager classification. Employers filing for this title must certify a prevailing wage through DOL before USCIS adjudicates the petition. Most sponsoring companies are large tech, finance, and healthcare firms with established immigration programs.
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Get Access To All JobsAbout Glean:
Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles.
At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level.
Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality.
If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company.
About the Role:
Glean is seeking a Machine Learning Engineer to improve the quality of our AI Assistant and autonomous agents. This role sits at the intersection of production machine learning, LLM-powered systems, and product engineering, with a focus on building, evaluating, and iterating on assistant experiences that are useful, reliable, and grounded in real enterprise workflows.
You will work on applied problems across agent quality, evaluation, personalization, retrieval, and orchestration. The ideal person is excited by shipping production systems, not pure research, and wants to help shape how Glean’s assistant gets better over time through stronger signals, tighter feedback loops, and better end-to-end execution quality.
You will:
- Build and improve ML and LLM-powered systems that raise the quality of Glean’s AI Assistant and autonomous agents across real user workflows.
- Design evaluation, benchmarking, and monitoring loops to measure assistant quality, model quality, and end-to-end system performance.
- Develop and iterate on signals, prompts, workflows, and model-driven logic that improve reasoning, planning, personalization, and task completion quality.
- Work across areas such as RAG, semantic search, recommendation-style systems, post-training or reinforcement learning, and agent orchestration where they materially improve product outcomes.
- Partner closely with product, design, and engineering teammates to understand customer pain points and ship high-quality production systems quickly.
- Contribute to the data and ML infrastructure needed to support robust experimentation, offline and online evaluation, and continuous model improvement.
About you:
- 2+ years of industry experience in machine learning, applied AI, or software engineering with significant ML ownership.
- Strong hands-on coding ability and a track record of shipping production systems, not just prototypes or research projects.
- Experience in one or more of the following areas: LLM applications, NLP, search, retrieval, recommendations, evaluation frameworks, agent systems, or personalization.
- Comfort working across both modeling and product engineering details, including experimentation, quality measurement, and production iteration.
- Proficiency in common ML tooling and strong software engineering fundamentals in languages such as Python, Go, Java, or C++.
- A pragmatic, product-minded approach. You know when to use sophisticated ML techniques and when simple, reliable systems are the better answer.
- A proactive, low-ego working style and excitement about learning quickly in a high-velocity environment.
Location:
- This role is hybrid (4 days a week in our San Francisco office)
Compensation & Benefits:
The standard base salary range for this position is $180,000 - $205,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.
We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused.
We’re committed to building and sustaining a diverse, inclusive workplace. We strive to attract and retain people with a wide range of backgrounds, experiences, and perspectives, and we do not discriminate on the basis of gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.
LI-HYBRID
AI-First Mindset at Glean:
At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required.
Global Data Privacy Notice for Job Candidates and Applicants:
Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy. By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement.
About Glean:
Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles.
At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level.
Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality.
If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company.
About the Role:
Glean is seeking a Machine Learning Engineer to improve the quality of our AI Assistant and autonomous agents. This role sits at the intersection of production machine learning, LLM-powered systems, and product engineering, with a focus on building, evaluating, and iterating on assistant experiences that are useful, reliable, and grounded in real enterprise workflows.
You will work on applied problems across agent quality, evaluation, personalization, retrieval, and orchestration. The ideal person is excited by shipping production systems, not pure research, and wants to help shape how Glean’s assistant gets better over time through stronger signals, tighter feedback loops, and better end-to-end execution quality.
You will:
- Build and improve ML and LLM-powered systems that raise the quality of Glean’s AI Assistant and autonomous agents across real user workflows.
- Design evaluation, benchmarking, and monitoring loops to measure assistant quality, model quality, and end-to-end system performance.
- Develop and iterate on signals, prompts, workflows, and model-driven logic that improve reasoning, planning, personalization, and task completion quality.
- Work across areas such as RAG, semantic search, recommendation-style systems, post-training or reinforcement learning, and agent orchestration where they materially improve product outcomes.
- Partner closely with product, design, and engineering teammates to understand customer pain points and ship high-quality production systems quickly.
- Contribute to the data and ML infrastructure needed to support robust experimentation, offline and online evaluation, and continuous model improvement.
About you:
- 2+ years of industry experience in machine learning, applied AI, or software engineering with significant ML ownership.
- Strong hands-on coding ability and a track record of shipping production systems, not just prototypes or research projects.
- Experience in one or more of the following areas: LLM applications, NLP, search, retrieval, recommendations, evaluation frameworks, agent systems, or personalization.
- Comfort working across both modeling and product engineering details, including experimentation, quality measurement, and production iteration.
- Proficiency in common ML tooling and strong software engineering fundamentals in languages such as Python, Go, Java, or C++.
- A pragmatic, product-minded approach. You know when to use sophisticated ML techniques and when simple, reliable systems are the better answer.
- A proactive, low-ego working style and excitement about learning quickly in a high-velocity environment.
Location:
- This role is hybrid (4 days a week in our San Francisco office)
Compensation & Benefits:
The standard base salary range for this position is $180,000 - $205,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.
We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused.
We’re committed to building and sustaining a diverse, inclusive workplace. We strive to attract and retain people with a wide range of backgrounds, experiences, and perspectives, and we do not discriminate on the basis of gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.
LI-HYBRID
AI-First Mindset at Glean:
At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required.
Global Data Privacy Notice for Job Candidates and Applicants:
Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy. By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement.
See all 2,656+ H-1B Visa Machine Learning Manager Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B Visa Machine Learning Manager Jobs.
Get Access To All JobsTips for Finding H-1B Visa Sponsorship as a Machine Learning Manager
Map your credentials to SOC codes
Machine Learning Manager maps to SOC 11-3021 in DOL's classification system. Pull the O*NET profile for that code before interviews so you can speak precisely to how your experience matches the specialty occupation definition USCIS applies.
Target employers with LCA filing history
Search Migrate Mate to filter Machine Learning Manager roles by employers who have already filed Labor Condition Applications for this title. Prior LCA filings signal an active sponsorship program, not a first-time attempt that stalls in legal review.
Ask about cap-exempt status early
Universities, nonprofits affiliated with higher education, and certain research organizations are cap-exempt, meaning your H-1B petition bypasses the annual lottery. If you're open to these employers, confirm their cap-exempt status before accepting an offer.
Negotiate premium processing into your offer
USCIS premium processing cuts adjudication to 15 business days. For a Machine Learning Manager role with a defined project start date, ask the hiring team to commit to premium processing in writing before you sign, since the employer controls that decision.
Verify the prevailing wage tier before you negotiate
Run the OFLC Wage Search for SOC 11-3021 in your target metro before salary discussions. DOL assigns four wage levels, and the LCA must certify the offered wage meets at least Level I. Knowing the tiers prevents you from accepting an offer the employer can't legally certify.
Document your managerial scope for RFE defense
USCIS sometimes issues Requests for Evidence on manager titles, questioning whether the role is supervisory or hands-on technical. Collect org charts, headcount data, and budget authority documentation before your employer files, so the response is ready if adjudication stalls.
H-1B Visa Machine Learning Manager: Frequently Asked Questions
Does a Machine Learning Manager role qualify as an H-1B specialty occupation?
Yes. Machine Learning Manager qualifies because the position typically requires a bachelor's degree or higher in computer science, engineering, mathematics, or a closely related field. USCIS evaluates the specific job duties, not just the title, so your employer's job description must demonstrate that a specialized degree is the normal minimum for the position.
Which employers sponsor H-1B visas for Machine Learning Manager positions?
Large technology companies, financial institutions with quantitative research divisions, and healthcare organizations running predictive analytics programs are the most consistent H-1B sponsors for this title. Migrate Mate lets you browse Machine Learning Manager roles filtered by employers with verified LCA filing history, so you're not applying blind to companies that have never sponsored this job category.
How does the H-1B lottery affect my timeline for starting a Machine Learning Manager role?
The annual H-1B cap lottery opens in March for an October 1 start date. If you're not selected, you can't begin work in cap-subject H-1B status until the following fiscal year at earliest. Cap-exempt employers, such as universities or affiliated nonprofits, skip the lottery entirely and can file at any point in the year.
Can I transfer my H-1B to a new employer if I change Machine Learning Manager roles?
Yes. Under H-1B portability rules, you can begin working for a new employer as soon as that employer files a transfer petition, without waiting for USCIS approval. The new employer must file a new LCA certifying the prevailing wage for the Machine Learning Manager title at the new work location before submitting the transfer petition.
What documentation does my employer need to sponsor my H-1B as a Machine Learning Manager?
Your employer needs a certified LCA from DOL, your academic transcripts and any credential evaluations, a detailed job description tied to the SOC 11-3021 classification, and evidence of your qualifications. If your degree is from outside the U.S., a credential evaluation establishing equivalency to a U.S. bachelor's degree is required before USCIS will accept the petition.