Green Card Senior Mlops Engineer Jobs
Senior MLOps Engineer roles qualify for EB-2 or EB-3 green card sponsorship through the PERM labor certification process, which requires your employer to document that no qualified U.S. worker is available. Roles requiring ML infrastructure design, model deployment pipelines, and production monitoring typically meet the specialty occupation standard that supports both categories.
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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 Senior Mlops Engineer Jobs
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Get Access To All JobsTips for Finding Green Card Sponsorship as a Senior Mlops Engineer
Align your credentials to EB-2 requirements
A master's degree in computer science, machine learning, or a related field positions you for EB-2 eligibility. If you hold only a bachelor's degree, document five or more years of progressive MLOps experience to satisfy the advanced-degree equivalent standard under PERM.
Target employers with active PERM filing history
Search the DOL's OFLC Wage Search database to identify companies that have previously filed PERM applications for MLOps or machine learning engineering roles. Repeated filings signal an established internal sponsorship process rather than a one-off case.
Verify your role's SOC code before the offer
MLOps Engineer positions are commonly classified under SOC 15-2051 or 15-1244 depending on the employer's framing. Confirm which code your offer letter will use, since the SOC code determines the prevailing wage tier DOL applies during PERM labor certification.
Use Migrate Mate to find sponsoring employers fast
Search for Senior MLOps Engineer roles with confirmed green card sponsorship history using Migrate Mate. The platform surfaces employers who have filed PERM or I-140 petitions for comparable ML infrastructure roles, saving you from cold-outreach to companies with no sponsorship track record.
Negotiate PERM timeline into your employment offer
Ask your employer to initiate the PERM prevailing wage determination request with DOL within your first three months. PERM processing at DOL currently runs six to eighteen months before the I-140 stage, so starting early directly affects how quickly your priority date is established.
Prepare your O*NET occupation documentation early
USCIS and your employer's immigration counsel will reference the O*NET profile for your role to support the specialty occupation determination. Gather job descriptions, project portfolios, and academic transcripts that map your duties to the technical requirements listed there before the audit process begins.
Green Card Senior Mlops Engineer: Frequently Asked Questions
Does a Senior MLOps Engineer role qualify for EB-2 or EB-3 sponsorship?
Most Senior MLOps Engineer positions qualify for EB-2 if the role requires a master's degree or equivalent, or EB-3 if it requires at least a bachelor's degree in a specific technical field. Your employer's immigration counsel determines the category based on the actual job duties, the minimum education requirement in the job description, and the prevailing wage tier assigned by DOL during PERM.
How does PERM green card sponsorship differ from H-1B for this role?
H-1B visa is a temporary status capped at six years without an approved I-140, subject to an annual lottery, and tied to a specific employer. PERM-based green card sponsorship is permanent, has no annual lottery, and once your I-140 is approved your priority date is locked even if you change employers under AC21 portability. For MLOps engineers from most countries outside India and China, EB-3 wait times are short or negligible.
What documentation should I prepare for the PERM specialty occupation determination?
You'll need official transcripts showing a bachelor's degree or higher in computer science, software engineering, or a closely related field, along with a detailed resume that maps your MLOps experience to the job duties in the offer letter. If your degree is from outside the U.S., obtain a credential evaluation from a NACES-member organization before your employer files the PERM application with DOL.
Where can I find Senior MLOps Engineer jobs where the employer already sponsors green cards?
Migrate Mate aggregates Senior MLOps Engineer roles from employers with verified PERM and I-140 filing history, so you can filter for companies that have already navigated the green card process for comparable positions. This avoids the common scenario of negotiating sponsorship from scratch with an employer who has no internal process for it.
Can I switch employers after my PERM is approved but before I get my green card?
Yes, under AC21 portability you can change to a same or similar occupational role after your I-485 adjustment of status application has been pending for 180 days or more. MLOps and machine learning engineering roles generally fall within the same SOC code family, which supports portability claims. Notify USCIS of the job change and keep documentation showing the new role is substantially similar to the one on your approved I-140.