H-1B Visa Mlops Engineer Jobs
MLOps Engineer roles qualify for H-1B visa sponsorship as specialty occupations requiring at least a bachelor's degree in computer science, data engineering, or a related field. Employers across cloud infrastructure, fintech, and AI product companies file H-1B petitions for this role, with LCA prevailing wage levels typically falling at Level II or III under the SOC code for Software Developers.
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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 H-1B Visa Mlops Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B Visa Mlops Engineer Jobs.
Get Access To All JobsTips for Finding H-1B Visa Sponsorship as a Mlops Engineer
Match your SOC code before applying
MLOps Engineers are typically filed under Software Developers or Computer Occupations SOC codes. Check your offer letter's job duties align with the SOC code your employer plans to use, since a mismatch can trigger an RFE during adjudication.
Verify prevailing wage before accepting offers
Use the OFLC Wage Search to look up the prevailing wage for the SOC code and location in your offer. If the offered salary falls below Level II, your employer's LCA certification is at risk, which stalls the H-1B petition entirely.
Target employers with LCA filings for ML roles
Use Migrate Mate to filter for employers with verified LCA filing history in machine learning and MLOps roles. This cuts out companies that express interest but have no demonstrated track record of H-1B sponsorship for technical AI positions.
Document cloud platform certifications before filing
USCIS scrutinizes specialty occupation for MLOps roles because the title is still relatively new. AWS, GCP, or Azure ML certifications, combined with a transcript showing relevant coursework, strengthen the specialty occupation argument in your I-129 petition.
Clarify who files the LCA with your recruiter
Some employers outsource immigration to a third-party vendor who may delay DOL LCA submission. Confirm during the offer stage that the employer will file the LCA directly or through counsel before your start date, not after onboarding begins.
Use the cap-gap window if your OPT expires mid-pipeline
If you're on OPT and your H-1B is selected in the lottery but not yet approved by October 1, the cap-gap provision automatically extends your work authorization. Confirm your employer knows to keep your start date at October 1 or later to stay within this window.
H-1B Visa Mlops Engineer: Frequently Asked Questions
Does an MLOps Engineer role qualify as a specialty occupation for H-1B purposes?
Yes. MLOps Engineer roles qualify as specialty occupations under H-1B rules when the position requires at least a bachelor's degree in computer science, data engineering, software engineering, or a closely related field. USCIS looks at whether the degree requirement is standard for the role in the industry, so job descriptions that list a specific technical degree as required, not preferred, are stronger petition candidates. Roles that accept any bachelor's degree regardless of field are harder to defend.
Which employers sponsor H-1B visas for MLOps Engineers?
Cloud infrastructure companies, AI product startups, financial services firms, and large tech employers are the most consistent H-1B sponsors for MLOps roles. Employers that run production ML systems at scale tend to have established immigration processes and file LCAs regularly under relevant SOC codes. Migrate Mate lets you browse MLOps Engineer roles filtered by employers with verified H-1B filing history, so you're not applying blind.
What SOC code does USCIS typically use for MLOps Engineer H-1B petitions?
Employers most commonly file MLOps Engineer petitions under the Software Developers SOC code or, in some cases, Computer Occupations. The choice affects the prevailing wage level your employer must certify on the LCA. Ask your employer's immigration counsel which SOC code they plan to use before the LCA is submitted, since switching after certification requires refiling with DOL.
Can I transfer my H-1B to a new employer if my MLOps role changes scope significantly?
Yes, you can transfer your H-1B to a new employer through H-1B portability without waiting for full approval, as long as you file the new petition while your current status is valid and you've been in lawful H-1B status for at least one year. If the new role involves a materially different job description, the new employer files a fresh I-129 petition with a new LCA rather than a simple transfer. Portability lets you start the new job once the transfer petition is filed, not after approval.
How does the H-1B lottery affect MLOps Engineers trying to plan their job search timeline?
The H-1B cap lottery opens in March each year for an October 1 start date, so MLOps Engineers on OPT need a job offer in hand by late February to be included. If you're not selected, your employer can reenter you in subsequent years or explore cap-exempt employers, such as universities and certain nonprofit research organizations, which can file H-1B petitions year-round outside the cap. O*NET lists MLOps-adjacent occupations if you need to verify job zone classification for planning purposes.