Green Card Mlops Engineer Jobs
MLOps Engineer roles qualify for EB-2 and EB-3 green card sponsorship through the PERM labor certification process, which requires your employer to demonstrate no qualified U.S. workers are available. Most positions require a bachelor's or master's degree in computer science or a related field, making EB-2 the common pathway for advanced-degree professionals in this specialty.
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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 Green Card Mlops Engineer Jobs
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Get Access To All JobsTips for Finding Green Card Sponsorship as a Mlops Engineer
Document your ML infrastructure work precisely
PERM requires a detailed job description tied to a specific SOC code. Gather records of your MLOps work across model deployment pipelines, CI/CD automation, and cloud orchestration so your employer's attorney can define the role narrowly enough to defend prevailing-wage levels.
Check prevailing wages before accepting offers
Use the OFLC Wage Search to look up the prevailing wage for MLOps Engineer roles by location before you sign an offer. If the offered salary falls below the DOL wage level for your metro area, the PERM filing is at risk from the start.
Target employers with active PERM filing history
Search for companies that have filed PERM applications for MLOps or machine learning engineering roles in the past two years. Employers already familiar with the process move faster and are less likely to abandon sponsorship mid-filing due to cost or complexity concerns.
Use Migrate Mate to find verified green card sponsors
Search Migrate Mate to filter MLOps Engineer roles by employers with documented EB-2 and EB-3 sponsorship history. You'll see which companies have completed PERM filings for similar titles, so you can prioritize applications where green card sponsorship is already part of the hiring process.
Negotiate sponsorship terms before your start date
Ask your future employer to confirm in writing whether PERM filing is included in your offer, who covers legal fees, and the expected start timeline. Many MLOps candidates lose months assuming sponsorship is automatic when it requires a separate internal approval.
Understand EB-2 NIW as a self-petition fallback
If your employer won't sponsor PERM, National Interest Waiver allows EB-2-eligible candidates to self-petition without employer sponsorship. MLOps professionals working on critical AI infrastructure, healthcare models, or national security systems can build a strong NIW case through USCIS.
Green Card Mlops Engineer: Frequently Asked Questions
Does an MLOps Engineer role qualify for EB-2 or EB-3 green card sponsorship?
Most MLOps Engineer positions qualify for either EB-2 or EB-3 depending on the degree requirement the employer sets in the job description. Roles requiring a master's degree or equivalent typically fall under EB-2. Positions requiring a bachelor's degree in computer science, data engineering, or a related field generally qualify under EB-3. The PERM job description, not your personal credentials, determines the category.
How does green card sponsorship differ from H-1B for MLOps Engineers?
H-1B visa is a temporary work visa with a three-year initial period and annual lottery exposure. EB-2 and EB-3 green card sponsorship through PERM leads to permanent residency with no annual cap at the petition level for most nationalities. The PERM process takes 12 to 24 months before the I-140 stage, but once approved you're on a path that H-1B renewals can't guarantee.
Where can I find MLOps Engineer jobs that include green card sponsorship?
Migrate Mate lets you search specifically for MLOps Engineer roles at employers with verified PERM and EB-2 or EB-3 filing history. Unlike general job boards, Migrate Mate filters by sponsorship type so you're applying to companies that have already navigated the DOL and USCIS process for similar roles rather than hoping sponsorship comes up during negotiations.
What SOC code applies to MLOps Engineers for PERM filings?
MLOps Engineer roles are most commonly filed under SOC 15-1244 (Computer Network Architects) or 15-1252 (Software Developers) depending on the primary responsibilities. Roles centered on model deployment infrastructure and platform engineering may also use 15-1299 (Computer Occupations, All Other). The SOC code your employer selects determines the prevailing wage level DOL will require, so the job description must match the code accurately.
Can my employer start the PERM process while I'm on H-1B?
Yes, and starting PERM while you're on H-1B is the standard approach for most MLOps Engineers. USCIS allows H-1B holders to pursue concurrent green card sponsorship without affecting their nonimmigrant status. Filing PERM early also protects your priority date, which becomes important if you're from a high-backlog country like India or China where EB-2 and EB-3 wait times extend significantly.