Senior Mlops Engineer Jobs in USA with Visa Sponsorship
Senior MLOps Engineers are strong candidates for H-1B visa and O-1 visa sponsorship. The role qualifies as a specialty occupation requiring a bachelor's degree or higher in computer science, engineering, or a related field, and employers routinely sponsor across both cap-subject and cap-exempt pathways. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsOur Mission: 6sense's mission is to multiply what matters: growth, retention, and efficiency. We envision a future where companies, teams and people reach their full potential.
Our People: People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging. Every 6sensor plays a part in defining the future of our industry-leading technology. 6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers. We want 6sense to be the best chapter of your career.
Senior MLOps Engineer, Forward Deployed
TEAM Forward Deployed Engineering (FDE), Predictive track, in Data Science & AI Engineering
REPORTS TO MLOps engineering lead
LEVEL Senior (6+ years)
LOCATION United States
THE ROLE IN ONE PARAGRAPH This is a hands-on MLOps engineering role. You will work across model training and refresh workflows, APIs, orchestration, data pipelines, production monitoring, and user-facing experiences such as model dashboards and training controls. When a customer needs a better model, a refresh workflow, or visibility into model health, you will help make it real: build the experience, connect it to reliable backend systems, validate the outcome, and operate it safely in production. You'll be our first MLOps engineer in the US, working closely with the MLOps team in India so customer work reaches production in days, not weeks. Most of your work is behind the scenes, with customer conversations when the technical details matter.
Build for the customer
- Productionize customer-tailored predictive models, configurations, and scoring workflows with validation, release, monitoring, and rollback paths.
- Build and improve UI workflows for training triggers, model refreshes, validation outcomes, model status, and operational troubleshooting.
- Enhance model dashboards that make model health, score changes, data quality, refresh status, and production issues understandable to internal users and customers.
- Build or modify the APIs, backend services, data contracts, and orchestration workflows behind those experiences.
- Automate model retraining, validation, promotion, and deployment workflows for customer engagements.
- Create safeguards for data quality, score distributions, threshold changes, and edge cases before model changes reach production.
- Monitor models and services for drift, data shifts, reliability issues, and operational regressions.
Productize for everyone
- Turn repeatable customer solutions into reusable product and platform capabilities with Product, Data Science, and Platform Engineering.
- Build deployment templates, reusable workflow components, and configuration-driven interfaces that support many customers without one-off operational work.
- Create self-service or assisted tools for teams to initiate training, monitor refreshes, review validation results, and troubleshoot model issues safely.
- Improve the MLOps platform where needed, including Kubernetes workloads, compute, pipelines, CI/CD, deployment tooling, and observability.
- Establish clear runbooks, release checklists, audit trails, and support processes for model operations.
- Identify recurring customer pain points and convert them into product requirements, platform improvements, or durable operational tooling.
What You'll Bring
- 6+ years of experience in MLOps, ML platform engineering, backend engineering, full-stack engineering, or related production engineering roles.
- Experience owning machine-learning systems in production, including training, deployment, monitoring, retraining, and incident response.
- Strong Python and SQL skills, including APIs, backend services, asynchronous workflows, testing, CI/CD, and observability.
- Experience building or materially contributing to internal tools, operational dashboards, or customer-facing product workflows.
- Comfortable debugging across the stack: UI, APIs, workflows, data pipelines, model artifacts, and production infrastructure.
- Experience releasing changes safely across multiple customers or production environments, including staged rollout, impact checks, and rollback.
- Working knowledge of Kubernetes and cloud infrastructure.
- Strong communication and judgment when balancing customer urgency, usability, reliability, and long-term maintainability.
- Nice to have: Fast API and Frontend experience.
- Airflow, Spark, MLflow, model registries, feature pipelines, or workflow orchestration.
- AWS, S3, Hive, distributed compute, and multi-tenant SaaS experience.
- Forward-deployed, consulting, solutions, implementation, or customer-facing engineering experience.
- B2B SaaS, go-to-market data, or predictive modeling experience.
What Success Looks Like
Within 3 months
- Understand the predictive-model lifecycle and improve at least one meaningful deployment, support, or visibility bottleneck.
- Bring clear release, monitoring, and support plans to active customer model engagements.
Within 6 months
- Common training, refresh, validation, and deployment workflows are materially more automated.
- Model dashboards or internal workflows provide clear visibility into model status, health, and refresh progress.
- Customer model changes move to production faster, with stronger operational controls.
Within 12 months
- At least one customer-specific solution has become a reusable product or platform capability.
- The team can reliably deliver and operate customer-tailored models without fragile manual processes or overnight handoffs.
Why this role is different: You won’t only maintain ML infrastructure. You’ll build the product experiences and production systems that make predictive models easier to launch, understand, refresh, and trust—then turn what works for one customer into something that benefits many.
Base Salary Range: $191,475.00 - $260,830.00. The base salary range represents the anticipated low and high end of the base salary range for this position. Actual salaries may vary and may be above or below the range based on various factors, including but not limited to work location and experience. The base salary is one component of 6sense’s total compensation package for this position. Other compensation may include a bonus program or commission plan, and stock options if approved by 6sense’s board. In addition, 6sense provides a variety of benefits, including generous health insurance coverage, life, and disability insurance, a 401K employer matching program, paid holidays, self-care days, and paid time off (PTO).
Our Benefits: Full-time employees can take advantage of health coverage, paid parental leave, generous paid time-off and holidays, quarterly self-care days off, and stock options. We’ll make sure you have the equipment and support you need to work and connect with your teams, at home or in one of our offices. We have a growth mindset culture that is represented in all that we do, from onboarding through to numerous learning and development initiatives including access to our LinkedIn Learning platform. Employee well-being is also top of mind for us. We host quarterly wellness education sessions to encourage self-care and personal growth. From wellness days to ERG-hosted events, we celebrate and energize all 6sense employees and their backgrounds.
Equal Opportunity Employer: 6sense is an Equal Employment Opportunity and Affirmative Action Employers. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status. If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to jobs@6sense.com.
We are aware of recruiting impersonation attempts that are not affiliated with 6sense in any way. All email communications from 6sense will originate from the @6sense.com domain. We will not initially contact you via text message and will never request payments. If you are uncertain whether you have been contacted by an official 6sense employee, reach out to jobs@6sense.com.
Our Mission: 6sense's mission is to multiply what matters: growth, retention, and efficiency. We envision a future where companies, teams and people reach their full potential.
Our People: People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging. Every 6sensor plays a part in defining the future of our industry-leading technology. 6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers. We want 6sense to be the best chapter of your career.
Senior MLOps Engineer, Forward Deployed
TEAM Forward Deployed Engineering (FDE), Predictive track, in Data Science & AI Engineering
REPORTS TO MLOps engineering lead
LEVEL Senior (6+ years)
LOCATION United States
THE ROLE IN ONE PARAGRAPH This is a hands-on MLOps engineering role. You will work across model training and refresh workflows, APIs, orchestration, data pipelines, production monitoring, and user-facing experiences such as model dashboards and training controls. When a customer needs a better model, a refresh workflow, or visibility into model health, you will help make it real: build the experience, connect it to reliable backend systems, validate the outcome, and operate it safely in production. You'll be our first MLOps engineer in the US, working closely with the MLOps team in India so customer work reaches production in days, not weeks. Most of your work is behind the scenes, with customer conversations when the technical details matter.
Build for the customer
- Productionize customer-tailored predictive models, configurations, and scoring workflows with validation, release, monitoring, and rollback paths.
- Build and improve UI workflows for training triggers, model refreshes, validation outcomes, model status, and operational troubleshooting.
- Enhance model dashboards that make model health, score changes, data quality, refresh status, and production issues understandable to internal users and customers.
- Build or modify the APIs, backend services, data contracts, and orchestration workflows behind those experiences.
- Automate model retraining, validation, promotion, and deployment workflows for customer engagements.
- Create safeguards for data quality, score distributions, threshold changes, and edge cases before model changes reach production.
- Monitor models and services for drift, data shifts, reliability issues, and operational regressions.
Productize for everyone
- Turn repeatable customer solutions into reusable product and platform capabilities with Product, Data Science, and Platform Engineering.
- Build deployment templates, reusable workflow components, and configuration-driven interfaces that support many customers without one-off operational work.
- Create self-service or assisted tools for teams to initiate training, monitor refreshes, review validation results, and troubleshoot model issues safely.
- Improve the MLOps platform where needed, including Kubernetes workloads, compute, pipelines, CI/CD, deployment tooling, and observability.
- Establish clear runbooks, release checklists, audit trails, and support processes for model operations.
- Identify recurring customer pain points and convert them into product requirements, platform improvements, or durable operational tooling.
What You'll Bring
- 6+ years of experience in MLOps, ML platform engineering, backend engineering, full-stack engineering, or related production engineering roles.
- Experience owning machine-learning systems in production, including training, deployment, monitoring, retraining, and incident response.
- Strong Python and SQL skills, including APIs, backend services, asynchronous workflows, testing, CI/CD, and observability.
- Experience building or materially contributing to internal tools, operational dashboards, or customer-facing product workflows.
- Comfortable debugging across the stack: UI, APIs, workflows, data pipelines, model artifacts, and production infrastructure.
- Experience releasing changes safely across multiple customers or production environments, including staged rollout, impact checks, and rollback.
- Working knowledge of Kubernetes and cloud infrastructure.
- Strong communication and judgment when balancing customer urgency, usability, reliability, and long-term maintainability.
- Nice to have: Fast API and Frontend experience.
- Airflow, Spark, MLflow, model registries, feature pipelines, or workflow orchestration.
- AWS, S3, Hive, distributed compute, and multi-tenant SaaS experience.
- Forward-deployed, consulting, solutions, implementation, or customer-facing engineering experience.
- B2B SaaS, go-to-market data, or predictive modeling experience.
What Success Looks Like
Within 3 months
- Understand the predictive-model lifecycle and improve at least one meaningful deployment, support, or visibility bottleneck.
- Bring clear release, monitoring, and support plans to active customer model engagements.
Within 6 months
- Common training, refresh, validation, and deployment workflows are materially more automated.
- Model dashboards or internal workflows provide clear visibility into model status, health, and refresh progress.
- Customer model changes move to production faster, with stronger operational controls.
Within 12 months
- At least one customer-specific solution has become a reusable product or platform capability.
- The team can reliably deliver and operate customer-tailored models without fragile manual processes or overnight handoffs.
Why this role is different: You won’t only maintain ML infrastructure. You’ll build the product experiences and production systems that make predictive models easier to launch, understand, refresh, and trust—then turn what works for one customer into something that benefits many.
Base Salary Range: $191,475.00 - $260,830.00. The base salary range represents the anticipated low and high end of the base salary range for this position. Actual salaries may vary and may be above or below the range based on various factors, including but not limited to work location and experience. The base salary is one component of 6sense’s total compensation package for this position. Other compensation may include a bonus program or commission plan, and stock options if approved by 6sense’s board. In addition, 6sense provides a variety of benefits, including generous health insurance coverage, life, and disability insurance, a 401K employer matching program, paid holidays, self-care days, and paid time off (PTO).
Our Benefits: Full-time employees can take advantage of health coverage, paid parental leave, generous paid time-off and holidays, quarterly self-care days off, and stock options. We’ll make sure you have the equipment and support you need to work and connect with your teams, at home or in one of our offices. We have a growth mindset culture that is represented in all that we do, from onboarding through to numerous learning and development initiatives including access to our LinkedIn Learning platform. Employee well-being is also top of mind for us. We host quarterly wellness education sessions to encourage self-care and personal growth. From wellness days to ERG-hosted events, we celebrate and energize all 6sense employees and their backgrounds.
Equal Opportunity Employer: 6sense is an Equal Employment Opportunity and Affirmative Action Employers. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status. If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to jobs@6sense.com.
We are aware of recruiting impersonation attempts that are not affiliated with 6sense in any way. All email communications from 6sense will originate from the @6sense.com domain. We will not initially contact you via text message and will never request payments. If you are uncertain whether you have been contacted by an official 6sense employee, reach out to jobs@6sense.com.
See all 14,465+ Senior Mlops Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Senior Mlops Engineer roles.
Get Access To All JobsTips for Finding Visa Sponsorship as a Senior Mlops Engineer
Target cap-exempt employers first
Universities, nonprofit research institutions, and affiliated organizations can file H-1B petitions outside the annual lottery. For MLOps roles, these employers often run ML infrastructure teams and sponsor year-round without the April cap-subject filing window.
Document your specialized tech stack
USCIS approves H-1B petitions when the role requires a specific degree field. List every tool you work with, such as Kubeflow, MLflow, or Vertex AI, and connect each to how it demands specialized academic training beyond a general computer science background.
Use O-1A as a backup if you have a strong record
If you have published research, conference presentations, open-source contributions with significant adoption, or awards in machine learning, you may qualify for O-1A extraordinary ability. There is no lottery and no annual cap for this category.
Ask about L-1A and L-1B eligibility if you have an overseas employer
If your current employer has a U.S. office, an L-1B intracompany transfer for specialized knowledge is often faster than H-1B. MLOps engineers with proprietary internal platform knowledge frequently qualify. No lottery and no prevailing wage requirement for L-1B.
Clarify sponsorship scope during the offer stage
Some employers cover only the I-129 filing fee, not premium processing or legal fees. Confirm in writing what the employer will pay and whether they will support green card sponsorship after one year, which is common for senior engineering roles.
Browse verified sponsoring employers on Migrate Mate
Not every job listing makes sponsorship status obvious. Migrate Mate filters roles by verified sponsorship willingness, so you can focus applications on Senior MLOps positions where the employer has an active track record of filing H-1B petitions.
Frequently Asked Questions
Does a Senior MLOps Engineer role qualify as an H-1B specialty occupation?
Yes. Senior MLOps Engineer consistently qualifies as a specialty occupation because the position normally requires at least a bachelor's degree in computer science, software engineering, or a closely related field. USCIS looks at whether the degree requirement is standard for the industry. DOL LCA disclosure data shows thousands of approved H-1B visa petitions under MLOps and related ML infrastructure titles each year, confirming that employers and adjudicators treat the role as qualifying.
What degree do I need for H-1B sponsorship as an MLOps Engineer?
A bachelor's degree or higher in computer science, software engineering, mathematics, or a directly related technical field is the standard requirement. A degree in an unrelated field, even combined with strong MLOps experience, can complicate approval because USCIS requires the degree field to correspond to the role's duties. If your degree is adjacent rather than exact, an immigration attorney can build a theoretical argument using coursework relevance, but it adds risk. Equivalent experience, at the ratio of three years of work per one year of education, may substitute for a degree in some cases.
How likely is H-1B approval for an MLOps Engineer, and what are the main denial risks?
Approval rates for software and ML engineering roles are high relative to other occupations, but Requests for Evidence remain common when job duties are broadly written or the degree field is ambiguous. The most frequent RFE trigger for MLOps roles is a job description that reads as general software development rather than specialized ML infrastructure. Petitions with detailed duty descriptions linking specific tools and workflows to degree-level knowledge fare significantly better. Premium processing, which currently adjudicates within 15 business days, reduces uncertainty and is worth requesting for senior hires.
Where can I find Senior MLOps Engineer jobs that sponsor visas?
Migrate Mate is the recommended starting point. The platform lists Senior MLOps Engineer roles from employers with a verified sponsorship track record, so you are not guessing at sponsorship willingness from a standard job posting. Large tech companies, cloud platform teams at AWS, Google, and Microsoft, as well as AI-focused startups with institutional backing, are the most active sponsors in this category. Filtering by sponsorship status before applying saves significant time during a job search.
Can I stay in the U.S. and change MLOps employers if my H-1B is already approved?
Yes, H-1B portability allows you to start work with a new employer as soon as the new H-1B petition is filed, without waiting for approval, provided you have been in valid H-1B status and the new petition is non-frivolous. Your new employer files a fresh I-129 petition. You do not re-enter the lottery if you already hold H-1B status. This makes senior engineering roles with multiple competing offers more flexible than many candidates realize.
What is the prevailing wage requirement for sponsored Senior Mlops Engineer 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.