Machine Learning Engineer Visa Sponsorship Jobs in Texas
Texas is one of the top states for machine learning engineer visa sponsorship, with major hiring hubs in Austin, Dallas, and Houston. Employers like Dell Technologies, Texas Instruments, AT&T, and a growing cluster of AI-focused startups regularly sponsor international engineers. The state's tech sector spans semiconductor research, enterprise software, and energy-sector AI applications.
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Get Access To All JobsINTRODUCTION
Note: Looking for Ex-JPMC Candidates with OPT, CPT, GC or USC Visas for Fulltime only.
ROLE
Who You Are
You are a Senior AI/ML Full Stack Engineer who brings deep hands-on experience building production-grade AI applications with Java or Python. You've spent 7-10+ years mastering full-stack development and have moved confidently into agentic AI, RAG architectures, and LLM orchestration. You're just as comfortable designing a vector store retrieval pipeline as you are hardening a CI/CD deployment on AWS. You thrive in fast-paced, in-office environments where live coding and hands-on problem solving are part of the culture, and you're excited to bring responsible AI practices — guardrails, evaluation frameworks, and content filtering — into everything you build.
Responsibilities
- Design and build AI/ML applications end-to-end, from architecture through production deployment
- Implement RAG pipelines, including chunking strategies, embedding models, and vector store integration (Pinecone, OpenSearch, pgvector, FAISS)
- Build and orchestrate AI agents using frameworks such as LangChain, LlamaIndex, Semantic Kernel, or CrewAI
- Develop and deploy solutions using AWS Bedrock, Anthropic Claude models, and model invocation APIs
- Apply advanced prompt engineering techniques — system prompts, few-shot, chain-of-thought, tool use, structured outputs
- Build conversational AI experiences, including chatbots (text) and voicebots (speech-to-text, text-to-speech)
- Design and maintain APIs (REST, GraphQL), microservices, and event-driven architectures
- Own CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code for production systems
- Implement evaluation frameworks, guardrails, content filtering, and responsible AI practices across LLM-powered features
BASIC QUALIFICATIONS
What You Bring
- Bachelor's degree required
- 10+ years of software development experience (Java or Python), OR 7+ years if entirely full-stack + Agentic AI development experience
- 2+ years hands-on experience building AI/ML applications in production
- Strong proficiency with RAG architectures — chunking strategies, embedding models, vector stores (Pinecone, OpenSearch, pgvector, FAISS)
- Experience with AI orchestration frameworks: LangChain, LlamaIndex, Semantic Kernel, or CrewAI
- Hands-on experience with AWS Bedrock, Anthropic Claude models, and model invocation APIs
- Proven prompt engineering skills — system prompts, few-shot, chain-of-thought, tool use, structured outputs
- Experience building conversational AI: chatbots (text) and voicebots (speech-to-text, text-to-speech)
- Proficiency with AWS services (Lambda, Step Functions, API Gateway, S3, DynamoDB, SQS)
- Experience with CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code
- Strong understanding of API design (REST, GraphQL), microservices architecture, and event-driven systems
- Familiarity with evaluation frameworks for LLM outputs
- Experience with guardrails, content filtering, and responsible AI practices
INTRODUCTION
Note: Looking for Ex-JPMC Candidates with OPT, CPT, GC or USC Visas for Fulltime only.
ROLE
Who You Are
You are a Senior AI/ML Full Stack Engineer who brings deep hands-on experience building production-grade AI applications with Java or Python. You've spent 7-10+ years mastering full-stack development and have moved confidently into agentic AI, RAG architectures, and LLM orchestration. You're just as comfortable designing a vector store retrieval pipeline as you are hardening a CI/CD deployment on AWS. You thrive in fast-paced, in-office environments where live coding and hands-on problem solving are part of the culture, and you're excited to bring responsible AI practices — guardrails, evaluation frameworks, and content filtering — into everything you build.
Responsibilities
- Design and build AI/ML applications end-to-end, from architecture through production deployment
- Implement RAG pipelines, including chunking strategies, embedding models, and vector store integration (Pinecone, OpenSearch, pgvector, FAISS)
- Build and orchestrate AI agents using frameworks such as LangChain, LlamaIndex, Semantic Kernel, or CrewAI
- Develop and deploy solutions using AWS Bedrock, Anthropic Claude models, and model invocation APIs
- Apply advanced prompt engineering techniques — system prompts, few-shot, chain-of-thought, tool use, structured outputs
- Build conversational AI experiences, including chatbots (text) and voicebots (speech-to-text, text-to-speech)
- Design and maintain APIs (REST, GraphQL), microservices, and event-driven architectures
- Own CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code for production systems
- Implement evaluation frameworks, guardrails, content filtering, and responsible AI practices across LLM-powered features
BASIC QUALIFICATIONS
What You Bring
- Bachelor's degree required
- 10+ years of software development experience (Java or Python), OR 7+ years if entirely full-stack + Agentic AI development experience
- 2+ years hands-on experience building AI/ML applications in production
- Strong proficiency with RAG architectures — chunking strategies, embedding models, vector stores (Pinecone, OpenSearch, pgvector, FAISS)
- Experience with AI orchestration frameworks: LangChain, LlamaIndex, Semantic Kernel, or CrewAI
- Hands-on experience with AWS Bedrock, Anthropic Claude models, and model invocation APIs
- Proven prompt engineering skills — system prompts, few-shot, chain-of-thought, tool use, structured outputs
- Experience building conversational AI: chatbots (text) and voicebots (speech-to-text, text-to-speech)
- Proficiency with AWS services (Lambda, Step Functions, API Gateway, S3, DynamoDB, SQS)
- Experience with CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code
- Strong understanding of API design (REST, GraphQL), microservices architecture, and event-driven systems
- Familiarity with evaluation frameworks for LLM outputs
- Experience with guardrails, content filtering, and responsible AI practices
Machine Learning Engineer Job Roles in Texas
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Search Machine Learning Engineer Jobs in TexasMachine Learning Engineer Jobs in Texas: Frequently Asked Questions
Which companies sponsor visas for machine learning engineers in Texas?
Several large Texas-based employers have consistent track records of sponsoring machine learning engineers, including Dell Technologies, Texas Instruments, AT&T, and ExxonMobil, which applies ML to energy operations. Austin's tech corridor also includes Amazon, Apple, and Google offices that sponsor international engineers. Defense contractors near Dallas-Fort Worth, such as Lockheed Martin and Raytheon, hire ML engineers and have sponsored work visas through their established immigration programs.
Which visa types are most common for machine learning engineer roles in Texas?
The H-1B visa is the most common visa category for machine learning engineers in Texas, as the role typically requires at least a bachelor's degree in computer science, statistics, or a related field, satisfying the specialty occupation standard. Some engineers transition from F-1 OPT or STEM OPT extensions, which can provide up to three years of work authorization before H-1B visa sponsorship. Candidates with extraordinary research profiles may also qualify for the O-1A.
Which cities in Texas have the most machine learning engineer sponsorship jobs?
Austin leads Texas for machine learning engineer sponsorship activity, driven by its concentration of tech company campuses and AI startups. Dallas-Fort Worth follows closely, with strong demand from financial services firms, defense contractors, and telecom companies like AT&T. Houston has growing demand tied to energy-sector AI and medical research at institutions like the Texas Medical Center. San Antonio has a smaller but active market, particularly in cybersecurity and government-adjacent tech.
How to find machine learning engineer visa sponsorship jobs in Texas?
Migrate Mate lets you filter machine learning engineer roles specifically by visa sponsorship availability and state, making it straightforward to surface Texas-based employers actively hiring international candidates. Because sponsorship willingness is not always listed in standard job postings, using a platform built around that filter saves significant time. Roles in Austin, Dallas, and Houston appear frequently, and filtering by STEM-focused employers increases the likelihood of finding companies with established H-1B programs.
Are there any Texas-specific considerations for machine learning engineers seeking visa sponsorship?
Texas has no state income tax, which affects the prevailing wage calculation employers use when filing a Labor Condition Application, as wages are benchmarked to the local metropolitan area rather than adjusted for state taxes. Universities like UT Austin, Texas A&M, and Rice have strong ML research pipelines that feed directly into Texas employer sponsorship programs, making campus recruiting a meaningful channel. Texas also has a high concentration of STEM OPT-eligible employers willing to bridge candidates to H-1B sponsorship.
What is the prevailing wage for sponsored machine learning engineer jobs in Texas?
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.