AI ML Platform Jobs in USA with Visa Sponsorship
AI/ML Platform engineers are among the most actively sponsored roles in the U.S. tech industry. Employers regularly file H-1B visa and O-1 visa petitions for this specialty, and the work squarely qualifies as a specialty occupation under USCIS standards. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsTätigkeitsbereich: IT/Telekommunikation
Fachabteilung: Digital Customer Experience
Gesellschaft: Mercedes-Benz USA, LLC
Standort: Mercedes-Benz USA, LLC Corporate Headquarters, Atlanta, GA
Startdatum: sofort
Veröffentlichungsdatum: 22.07.2026
Stellennummer: MER0004694
Arbeitszeit: Vollzeit
About us
Mercedes-Benz USA is responsible for the sales, marketing and service of all Mercedes-Benz and Maybach products in the United States. In our people, you will find tremendous commitment to our corporate values: 'PRIDE = Passion, Respect, Integrity, Discipline, and Execution'. Our products and employees reflect this dedication. We are looking for diverse top-notch individuals to join the Mercedes-Benz Team and uphold these hallmarks.
Job Overview
The AI/ML Platform Engineer is responsible for the foundational platform capabilities that power AI and machine learning delivery across Mercedes-Benz USA. This role designs, builds, and operates shared AI/ML infrastructure, deployment pipelines, model lifecycle tooling, and reusable engineering services that enable data scientists, AI engineers, and business teams to develop and scale AI solutions efficiently.
As the platform foundation of the Applied AI Engineering & Operations team, this role supports classical machine learning, generative AI, agent-based solutions, and enterprise-scale analytics workloads. The ideal candidate combines strong platform engineering expertise, cloud experience, MLOps knowledge, and production operations experience with a passion for building reliable and scalable engineering foundations.
Responsibilities
AI/ML Platform Engineering & Delivery (60%)
- Design, build, and operate enterprise AI/ML platform capabilities and shared engineering services.
- Develop and maintain model deployment pipelines, model registry capabilities, experiment tracking, and foundational MLOps tooling.
- Create reusable platform components, templates, automation frameworks, and deployment standards that accelerate AI delivery.
- Provide the platform foundations supporting machine learning, generative AI, agent-based solutions, and AI productization initiatives.
- Design and manage multi-tenancy patterns, resource isolation strategies, and workload governance across teams and business domains.
- Ensure platform reliability, scalability, security, observability, and cost optimization across AI workloads.
- Drive operational excellence through monitoring, incident response, resiliency improvements, and continuous platform enhancements.
Platform Architecture & Engineering Standards (20%)
- Define and evolve platform architecture, deployment patterns, and engineering standards for AI/ML delivery.
- Evaluate emerging platform technologies and engineering approaches that improve scalability, performance, and developer productivity.
- Partner with architecture, infrastructure, security, and engineering teams to ensure alignment with enterprise standards.
- Provide technical leadership for platform investments, architecture decisions, and modernization initiatives.
Operational Excellence & Reliability (10%)
- Establish best practices for monitoring, logging, performance management, platform support, and operational readiness.
- Develop engineering standards, documentation, automation, and operational runbooks.
- Promote continuous improvement of platform reliability, supportability, and operational maturity.
Collaboration & Technical Leadership (10%)
- Collaborate with data scientists, AI engineers, architects, infrastructure teams, and business stakeholders.
- Provide technical mentorship and guidance across the AI Engineering organization.
- Support knowledge sharing, cross-training, and engineering excellence initiatives.
Technical Skills & Tools
Required
- Strong proficiency in Python, SQL, PySpark, and distributed data processing frameworks.
- Experience with Azure Databricks, including Unity Catalog, Delta Lake, MLflow, Feature Store, and Model Serving.
- Experience with Azure, AWS, or comparable cloud platforms supporting enterprise AI and machine learning workloads.
- Experience with model deployment pipelines, model registry management, experiment tracking, monitoring, and lifecycle management.
- Experience with CI/CD, workflow orchestration, and production AI platform operations.
- Experience with model serving, inference optimization, and scalable AI infrastructure.
- Experience with Docker, Kubernetes, Infrastructure as Code, and cloud-native deployment architectures.
- Experience supporting GPU-enabled workloads, distributed compute environments, and enterprise-scale platform operations.
- Experience with event-driven architectures, streaming technologies, and platform integration patterns.
- Experience with observability platforms, performance optimization, reliability engineering, and cloud cost management.
- Strong software engineering, automation, and production support practices.
Preferred Skillset
- Experience with Azure OpenAI, AWS Bedrock, or equivalent enterprise AI platforms.
- Experience with vector databases and retrieval technologies.
- Experience supporting generative AI and agent-based solutions at scale.
- Familiarity with Responsible AI, AI governance, security, and risk management frameworks.
Qualifications
Required
- Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related technical field.
- 8+ years of experience in software engineering, machine learning engineering, AI platform engineering, or related disciplines.
- Demonstrated experience designing, building, and operating enterprise AI/ML platforms.
- Strong understanding of cloud-native architectures, MLOps, deployment automation, monitoring, and production operations.
- Experience building reusable engineering frameworks, platform services, or shared infrastructure capabilities.
- Strong communication, collaboration, and stakeholder management skills.
Preferred Experience
- Master's degree in Computer Science, Engineering, AI/ML, or related field.
- Experience delivering enterprise-scale AI/ML platforms supporting multiple business domains.
- Experience operating in regulated or compliance-sensitive environments.
- Experience scaling platform engineering capabilities supporting machine learning, generative AI, and agent-based systems.
Additional Information
- Position requires regular collaboration with business, technology, and external partner teams across multiple time zones.
- Some travel required for team, partner, and business engagements.
- This role is part of MBUSA's Data Insights & AI organization and contributes to the company's long-term AI strategy and operating model.
EEO Statement
Mercedes-Benz USA is committed to fostering an inclusive environment that appreciates and leverages the diversity of our team. We provide equal employment opportunity (EEO) to all qualified applicants and employees without regard to race, color, ethnicity, gender, age, national origin, religion, marital status, veteran status, physical or other disability, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local law.
Benefits
- Mitarbeiterhandy möglich
- Mitarbeiter Events
- Gesundheitsmaßnahmen
- Betriebliche Altersversorgung
- Mobilitätsangebote
- Flexible Arbeitszeit möglich
- Mitarbeiterrabatte möglich
- Coaching
- Mitarbeiterbeteiligung möglich
- Parkplatz
- Gute Anbindung
- Barrierefreiheit
- Kinderbetreuung
- Kantine, Café
Kontakt
Mercedes-Benz USA, LLC
One Mercedes-Benz Drive
30328 Atlanta
Details zum Standort
MBUSA Talent Acquisition E-Mail: talent_acquisition@mbusa.com
Tätigkeitsbereich: IT/Telekommunikation
Fachabteilung: Digital Customer Experience
Gesellschaft: Mercedes-Benz USA, LLC
Standort: Mercedes-Benz USA, LLC Corporate Headquarters, Atlanta, GA
Startdatum: sofort
Veröffentlichungsdatum: 22.07.2026
Stellennummer: MER0004694
Arbeitszeit: Vollzeit
About us
Mercedes-Benz USA is responsible for the sales, marketing and service of all Mercedes-Benz and Maybach products in the United States. In our people, you will find tremendous commitment to our corporate values: 'PRIDE = Passion, Respect, Integrity, Discipline, and Execution'. Our products and employees reflect this dedication. We are looking for diverse top-notch individuals to join the Mercedes-Benz Team and uphold these hallmarks.
Job Overview
The AI/ML Platform Engineer is responsible for the foundational platform capabilities that power AI and machine learning delivery across Mercedes-Benz USA. This role designs, builds, and operates shared AI/ML infrastructure, deployment pipelines, model lifecycle tooling, and reusable engineering services that enable data scientists, AI engineers, and business teams to develop and scale AI solutions efficiently.
As the platform foundation of the Applied AI Engineering & Operations team, this role supports classical machine learning, generative AI, agent-based solutions, and enterprise-scale analytics workloads. The ideal candidate combines strong platform engineering expertise, cloud experience, MLOps knowledge, and production operations experience with a passion for building reliable and scalable engineering foundations.
Responsibilities
AI/ML Platform Engineering & Delivery (60%)
- Design, build, and operate enterprise AI/ML platform capabilities and shared engineering services.
- Develop and maintain model deployment pipelines, model registry capabilities, experiment tracking, and foundational MLOps tooling.
- Create reusable platform components, templates, automation frameworks, and deployment standards that accelerate AI delivery.
- Provide the platform foundations supporting machine learning, generative AI, agent-based solutions, and AI productization initiatives.
- Design and manage multi-tenancy patterns, resource isolation strategies, and workload governance across teams and business domains.
- Ensure platform reliability, scalability, security, observability, and cost optimization across AI workloads.
- Drive operational excellence through monitoring, incident response, resiliency improvements, and continuous platform enhancements.
Platform Architecture & Engineering Standards (20%)
- Define and evolve platform architecture, deployment patterns, and engineering standards for AI/ML delivery.
- Evaluate emerging platform technologies and engineering approaches that improve scalability, performance, and developer productivity.
- Partner with architecture, infrastructure, security, and engineering teams to ensure alignment with enterprise standards.
- Provide technical leadership for platform investments, architecture decisions, and modernization initiatives.
Operational Excellence & Reliability (10%)
- Establish best practices for monitoring, logging, performance management, platform support, and operational readiness.
- Develop engineering standards, documentation, automation, and operational runbooks.
- Promote continuous improvement of platform reliability, supportability, and operational maturity.
Collaboration & Technical Leadership (10%)
- Collaborate with data scientists, AI engineers, architects, infrastructure teams, and business stakeholders.
- Provide technical mentorship and guidance across the AI Engineering organization.
- Support knowledge sharing, cross-training, and engineering excellence initiatives.
Technical Skills & Tools
Required
- Strong proficiency in Python, SQL, PySpark, and distributed data processing frameworks.
- Experience with Azure Databricks, including Unity Catalog, Delta Lake, MLflow, Feature Store, and Model Serving.
- Experience with Azure, AWS, or comparable cloud platforms supporting enterprise AI and machine learning workloads.
- Experience with model deployment pipelines, model registry management, experiment tracking, monitoring, and lifecycle management.
- Experience with CI/CD, workflow orchestration, and production AI platform operations.
- Experience with model serving, inference optimization, and scalable AI infrastructure.
- Experience with Docker, Kubernetes, Infrastructure as Code, and cloud-native deployment architectures.
- Experience supporting GPU-enabled workloads, distributed compute environments, and enterprise-scale platform operations.
- Experience with event-driven architectures, streaming technologies, and platform integration patterns.
- Experience with observability platforms, performance optimization, reliability engineering, and cloud cost management.
- Strong software engineering, automation, and production support practices.
Preferred Skillset
- Experience with Azure OpenAI, AWS Bedrock, or equivalent enterprise AI platforms.
- Experience with vector databases and retrieval technologies.
- Experience supporting generative AI and agent-based solutions at scale.
- Familiarity with Responsible AI, AI governance, security, and risk management frameworks.
Qualifications
Required
- Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related technical field.
- 8+ years of experience in software engineering, machine learning engineering, AI platform engineering, or related disciplines.
- Demonstrated experience designing, building, and operating enterprise AI/ML platforms.
- Strong understanding of cloud-native architectures, MLOps, deployment automation, monitoring, and production operations.
- Experience building reusable engineering frameworks, platform services, or shared infrastructure capabilities.
- Strong communication, collaboration, and stakeholder management skills.
Preferred Experience
- Master's degree in Computer Science, Engineering, AI/ML, or related field.
- Experience delivering enterprise-scale AI/ML platforms supporting multiple business domains.
- Experience operating in regulated or compliance-sensitive environments.
- Experience scaling platform engineering capabilities supporting machine learning, generative AI, and agent-based systems.
Additional Information
- Position requires regular collaboration with business, technology, and external partner teams across multiple time zones.
- Some travel required for team, partner, and business engagements.
- This role is part of MBUSA's Data Insights & AI organization and contributes to the company's long-term AI strategy and operating model.
EEO Statement
Mercedes-Benz USA is committed to fostering an inclusive environment that appreciates and leverages the diversity of our team. We provide equal employment opportunity (EEO) to all qualified applicants and employees without regard to race, color, ethnicity, gender, age, national origin, religion, marital status, veteran status, physical or other disability, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local law.
Benefits
- Mitarbeiterhandy möglich
- Mitarbeiter Events
- Gesundheitsmaßnahmen
- Betriebliche Altersversorgung
- Mobilitätsangebote
- Flexible Arbeitszeit möglich
- Mitarbeiterrabatte möglich
- Coaching
- Mitarbeiterbeteiligung möglich
- Parkplatz
- Gute Anbindung
- Barrierefreiheit
- Kinderbetreuung
- Kantine, Café
Kontakt
Mercedes-Benz USA, LLC
One Mercedes-Benz Drive
30328 Atlanta
Details zum Standort
MBUSA Talent Acquisition E-Mail: talent_acquisition@mbusa.com
See all 56+ AI ML Platform Jobs
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Get Access To All JobsTips for Finding AI ML Platform Jobs
Frame your role as infrastructure, not research
AI/ML Platform is distinct from applied ML, you build the systems others train on. Emphasize distributed systems, MLOps pipelines, and compute orchestration in your resume. Visa officers and employers respond to infrastructure framing over vague 'AI' titles.
Target companies with established ML infrastructure teams
Large-scale tech firms and AI-native companies already have USCIS-approved H-1B specialty occupation classifications for platform engineering roles. Applying to companies with prior approval history significantly reduces petition risk and employer hesitation around sponsorship.
Clarify your degree field on every application
USCIS requires a degree in a directly related field, computer science, computer engineering, and electrical engineering qualify cleanly. If your degree is adjacent, document coursework in systems design or distributed computing to strengthen the specialty occupation argument.
Highlight specific platform technologies in your materials
Kubernetes, Ray, Kubeflow, Spark, and Triton inference server are concrete signals that distinguish platform engineers from generalist ML practitioners. Specificity in tooling helps employers justify the specialty occupation requirement to USCIS when petitioning.
Understand your OPT and cap-gap timeline before applying
If you're on F-1 OPT, H-1B cap season runs January through April for October start dates. Platform roles at cap-exempt universities or research labs allow year-round filing, worth targeting if your OPT timeline doesn't align with the annual lottery.
Document production-scale impact, not just model performance
Sponsoring employers need to justify a specialty occupation petition. Resume bullet points quantifying platform throughput, uptime improvements, or training cost reductions give immigration counsel concrete evidence that the role requires specialized technical expertise.
Frequently Asked Questions
Does an AI/ML Platform engineer role qualify as a specialty occupation for H-1B purposes?
Yes. AI/ML Platform engineering requires at least a bachelor's degree in computer science, computer engineering, or a closely related field, meeting USCIS's specialty occupation definition. The role involves designing distributed training infrastructure, ML pipelines, and serving systems that require theoretical and applied depth. Generalist or non-technical degrees are unlikely to satisfy the field-of-study requirement without substantial supporting documentation.
Which visa types are most common for AI/ML Platform engineers?
H-1B visa is the most common path, though it requires winning the annual lottery. O-1A is a realistic alternative for engineers with strong publication records, open-source contributions, or documented industry recognition. Australians can pursue the E-3 visa, which has no lottery and far lower competition. Canadians and Mexicans may qualify under the TN visa's computer systems analyst category. Browse open roles on Migrate Mate filtered by visa type to identify which employers are actively sponsoring.
Do employers actually sponsor H-1B visas for platform engineering roles?
Yes, and frequently. Companies like Google, Meta, Amazon, Microsoft, and AI-native firms such as Databricks and Anyscale have substantial H-1B filing histories for ML infrastructure and platform roles. DOL disclosure data confirms platform engineering titles appear consistently across LCA filings. The key filter is employer size and existing immigration infrastructure; smaller startups may be willing but slower to initiate the process.
What degree do I need to get sponsored for an AI/ML Platform role?
A bachelor's degree or higher in computer science, computer engineering, electrical engineering, or a directly related technical field is the standard requirement. Degrees in mathematics or physics may qualify with strong systems-focused coursework. If your degree is in a less directly related field, three years of relevant professional experience can substitute for one year of education under USCIS equivalency standards, though this requires detailed documentation and an immigration attorney's support.
How competitive is the H-1B lottery for AI/ML Platform engineers?
In FY 2025, USCIS received approximately 442,000 registrations for 85,000 available slots, resulting in a selection rate around 25%. Engineers with a U.S. master's degree or higher enter a separate pool first, improving odds modestly. If you don't get selected, cap-exempt employers, universities, nonprofit research labs, and certain government entities, can file outside the lottery. TN visa, E-3, and O-1 visas bypass the cap entirely and are worth evaluating if lottery odds are a concern.
What is the prevailing wage requirement for sponsored AI ML Platform 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.