Machine Learning Engineer Jobs in USA with Visa Sponsorship
Machine learning engineers who build the infrastructure to train, deploy, and monitor ML models at scale are critically needed by US companies operationalizing their data science investments. This role sits at the intersection of software engineering and data science - requiring expertise in feature engineering, model serving, distributed training, and monitoring - which makes it a strong specialty occupation for visa sponsorship. Employers ranging from FAANG to fintech to healthcare AI companies sponsor machine learning engineers because reliable ML infrastructure is what turns experimental models into revenue-generating products. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsINTRODUCTION
Optum Insight is improving the flow of health data and information to create a more connected system. We remove friction and drive alignment between care providers and payers, and ultimately consumers. Our deep expertise in the industry and innovative technology empower us to help organizations reduce costs while improving risk management, quality and revenue growth. Ready to help us deliver results that improve lives? Join us to start Caring. Connecting. Growing together.
ROLE AND RESPONSIBILITIES
Lead the autonomous medical coding enablement in a SaaS platform by integrating machine learning and LLMs. Working closely with data scientists and software engineers through data extraction, research, training, and deployment to create a scalable production solution that can handle millions of medical charts daily. You will be responsible for architecture decisions, code reviews, and coordinating across teams. You will work with cutting edge models, LLM, software, and tools in a fast paced environment.
You'll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.
Primary Responsibilities:
- Lead end-to-end ML projects: problem definition, data strategy, feature engineering, modeling, evaluation, deployment, and monitoring
- Architect scalable training and inference systems with strong SLAs, observability, and cost controls
- Establish experimentation rigor: offline evaluation, A/B testing, guardrails, power analysis, and causal insights
- Drive MLOps excellence: CI/CD for ML, reproducible pipelines, model registry and governance, automated retraining, drift/quality monitoring
- Collaborate with product and design to translate ambiguous goals into measurable ML problems; define success metrics and attribution
- Mentor and unblock engineers; conduct design and code reviews; set patterns for reliability, documentation, and testing
- Partner with data engineering on feature pipelines, data contracts, and online/offline parity; champion data quality
- Communicate tradeoffs and results to technical and non-technical stakeholders; influence roadmap and prioritization
- Optional focus areas depending on interest and business needs: LLM applications (RAG, fine-tuning, evaluation/guardrails), recommendations/ranking, anomaly detection, forecasting
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
BASIC QUALIFICATIONS
- Bachelor of Science or higher in Computer Science, Engineering, Statistics, or related field, or 4+ years of equivalent practical experience
- 7+ years building and operating ML systems in production with a track record of shipped impact
- 5+ years experience in C# or Python
- 5+ years experience in Azure
- 5+ years experience in supervised learning, feature engineering, evaluation methodology, bias/variance; deep learning and/or gradient boosting
- 5+ MLOps: CI/CD for ML, containers, Kubernetes/serverless inference, model registries, reproducibility, and model monitoring
- 2+ years experience LLMOps: prompt engineering, retrieval-augmented generation, fine-tuning, evaluation, and safety/guardrails
PREFERRED QUALIFICATIONS
- Domain experience in recommendations/ranking, time-series forecasting, anomaly detection, optimization, or reinforcement learning
- Privacy, security, and responsible AI practices (GDPR/CCPA, PII handling, fairness)
- Open-source contributions, publications, or patents; prior experience mentoring or tech leading small teams
- Excellent communication and product sense; able to scope ambiguous problems and align stakeholders
- Open-source contributions, publications, or patents; prior experience mentoring or tech leading small teams
- Data engineering for ML: ETL/ELT, SQL, distributed processing (e.g., Spark), and feature pipelines
- Experimentation: A/B testing design/analysis, guardrail metrics, basic causal inference
All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy.
COMPENSATION
- Salary Range: $112,700 to $193,200 annually based on full-time employment.
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. We comply with all minimum wage laws as applicable.
APPLICATION DEADLINE
This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.
UnitedHealth Group is a drug-free workplace. Candidates are required to pass a drug test before beginning employment.
OptumTechPJ
INTRODUCTION
Optum Insight is improving the flow of health data and information to create a more connected system. We remove friction and drive alignment between care providers and payers, and ultimately consumers. Our deep expertise in the industry and innovative technology empower us to help organizations reduce costs while improving risk management, quality and revenue growth. Ready to help us deliver results that improve lives? Join us to start Caring. Connecting. Growing together.
ROLE AND RESPONSIBILITIES
Lead the autonomous medical coding enablement in a SaaS platform by integrating machine learning and LLMs. Working closely with data scientists and software engineers through data extraction, research, training, and deployment to create a scalable production solution that can handle millions of medical charts daily. You will be responsible for architecture decisions, code reviews, and coordinating across teams. You will work with cutting edge models, LLM, software, and tools in a fast paced environment.
You'll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.
Primary Responsibilities:
- Lead end-to-end ML projects: problem definition, data strategy, feature engineering, modeling, evaluation, deployment, and monitoring
- Architect scalable training and inference systems with strong SLAs, observability, and cost controls
- Establish experimentation rigor: offline evaluation, A/B testing, guardrails, power analysis, and causal insights
- Drive MLOps excellence: CI/CD for ML, reproducible pipelines, model registry and governance, automated retraining, drift/quality monitoring
- Collaborate with product and design to translate ambiguous goals into measurable ML problems; define success metrics and attribution
- Mentor and unblock engineers; conduct design and code reviews; set patterns for reliability, documentation, and testing
- Partner with data engineering on feature pipelines, data contracts, and online/offline parity; champion data quality
- Communicate tradeoffs and results to technical and non-technical stakeholders; influence roadmap and prioritization
- Optional focus areas depending on interest and business needs: LLM applications (RAG, fine-tuning, evaluation/guardrails), recommendations/ranking, anomaly detection, forecasting
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
BASIC QUALIFICATIONS
- Bachelor of Science or higher in Computer Science, Engineering, Statistics, or related field, or 4+ years of equivalent practical experience
- 7+ years building and operating ML systems in production with a track record of shipped impact
- 5+ years experience in C# or Python
- 5+ years experience in Azure
- 5+ years experience in supervised learning, feature engineering, evaluation methodology, bias/variance; deep learning and/or gradient boosting
- 5+ MLOps: CI/CD for ML, containers, Kubernetes/serverless inference, model registries, reproducibility, and model monitoring
- 2+ years experience LLMOps: prompt engineering, retrieval-augmented generation, fine-tuning, evaluation, and safety/guardrails
PREFERRED QUALIFICATIONS
- Domain experience in recommendations/ranking, time-series forecasting, anomaly detection, optimization, or reinforcement learning
- Privacy, security, and responsible AI practices (GDPR/CCPA, PII handling, fairness)
- Open-source contributions, publications, or patents; prior experience mentoring or tech leading small teams
- Excellent communication and product sense; able to scope ambiguous problems and align stakeholders
- Open-source contributions, publications, or patents; prior experience mentoring or tech leading small teams
- Data engineering for ML: ETL/ELT, SQL, distributed processing (e.g., Spark), and feature pipelines
- Experimentation: A/B testing design/analysis, guardrail metrics, basic causal inference
All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy.
COMPENSATION
- Salary Range: $112,700 to $193,200 annually based on full-time employment.
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. We comply with all minimum wage laws as applicable.
APPLICATION DEADLINE
This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.
UnitedHealth Group is a drug-free workplace. Candidates are required to pass a drug test before beginning employment.
OptumTechPJ
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Get Access To All JobsTips for Finding Visa Sponsorship as a Machine Learning Engineer
Emphasize production engineering over research
MLE roles focus on deploying, scaling, and monitoring models in production - not just training them. Highlight experience with model serving frameworks like TensorFlow Serving, TorchServe, or Triton Inference Server to stand out.
Target companies with mature ML infrastructure teams
Google, Meta, Netflix, Uber, and Spotify have dedicated MLE teams that build and maintain production ML systems. These companies sponsor H-1B petitions under SOC 15-1252 and understand the engineering nature of the role.
Leverage your dual skill set in interviews
The MLE role bridges data science and software engineering, and that's your selling point. Strong candidates can discuss both model optimization and system design, which is rare and makes employers more willing to invest in sponsorship.
Build MLOps expertise to increase your value
Feature stores, experiment tracking, model monitoring, and automated retraining pipelines are critical MLE skills. Companies building serious ML products need engineers who can operationalize models, not just build prototypes.
Use STEM OPT to prove production reliability
With a STEM-eligible degree, you get up to 3 years of work authorization through OPT. ML systems require deep institutional knowledge to maintain - use that time to become indispensable to your team's production stack.
File under the right SOC code for engineering
MLE roles typically file under SOC 15-1252 (Software Developers), emphasizing the engineering and systems side of the work. This classification has strong precedent for H-1B approval - ensure your job description reflects the production engineering focus.
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Find Machine Learning Engineer JobsFrequently Asked Questions
What ML infrastructure skills are most valued by employers sponsoring machine learning engineers?
Experience with distributed training frameworks (PyTorch Distributed, DeepSpeed), model serving platforms (TensorFlow Serving, NVIDIA Triton, ONNX Runtime), and feature engineering tools (Feast, Tecton) are the most sought-after skills. Knowledge of GPU cluster management, inference cost optimization, and monitoring for data drift also carries significant weight. These specific technical requirements are exactly what make the visa petition strong, because they show the role requires specialized knowledge beyond general software engineering.
Do machine learning engineers need a PhD, or is a master's degree sufficient for sponsorship?
A master's degree is sufficient for the vast majority of ML engineering roles, and many positions only require a bachelor's in computer science or a related field. A PhD is more commonly expected for research-focused ML positions, not engineering roles focused on production systems. That said, a master's degree qualifies you for the additional 20,000 H-1B cap exemption slots reserved for U.S. advanced degree holders, which improves your lottery odds.
I have a research background but want to move into ML engineering. How does this affect sponsorship?
The transition is common and does not create visa issues. Your research background demonstrates the theoretical knowledge needed to make sound infrastructure decisions, while any production-adjacent work from your research (deploying models, building data pipelines, optimizing training runs) shows practical engineering capability. If you have a PhD, you benefit from the advanced degree H-1B exemption. The combination of theoretical depth from research and hands-on engineering skills can actually strengthen your petition.
How to find Machine Learning Engineer jobs with visa sponsorship?
To find Machine Learning Engineer jobs with visa sponsorship, use Migrate Mate, which specializes in connecting international talent with sponsoring employers. Focus on tech companies, startups, and research institutions that commonly hire ML engineers on H-1B, O-1, or other work visas. These employers often need specialized AI/ML expertise and are willing to sponsor qualified candidates with relevant experience in data science, neural networks, and algorithm development.
Which companies sponsor machine learning engineers most actively?
Companies operationalizing ML at scale are the most active sponsors. This includes large tech firms (Google, Meta, Amazon, Microsoft), ML-first product companies (Spotify, Netflix, Uber, Stripe), and AI infrastructure startups (Databricks, Anyscale, Weights & Biases). Fintech and healthcare AI companies are also growing sponsors. Look for employers whose products depend on reliable ML systems in production, as they are most motivated to invest in sponsorship for engineers who can bridge the gap between a trained model and a live product.
What prevailing wage levels typically apply to ML engineering roles?
ML engineering salaries typically place candidates at Level 3 or Level 4 of the Department of Labor prevailing wage system, which is favorable for visa petitions. Higher wage levels signal to USCIS that the role is senior and specialized, reducing the risk of a Request for Evidence. If an employer offers a salary at Level 1, that is a red flag for both immigration risk and fair compensation. You can check prevailing wages for your role and location on the DOL's Foreign Labor Certification Data Center.
What is the prevailing wage requirement for sponsored Machine Learning Engineer jobs?
When a U.S. employer sponsors a foreign worker for a work visa, they are legally required to pay at least the "prevailing wage", the average wage paid to workers in the same occupation, in the same geographic area, with similar experience. This is set by the Department of Labor to prevent employers from hiring foreign workers at below-market rates. The prevailing wage varies significantly by role, location, and experience level. For example, a machine learning engineer in California will have a different prevailing wage than the same role in a smaller state. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search Page.
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