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H1B Machine Learning Jobs in Austin, TX (NOW HIRING)

Kaav Inc

Java AI Engineer-C2C

Austin, TX · On-site

$51.25 - $70.50/hr

Java AI Engineer-NO OPT and H1B Location: Austin, TX/ Sunnyvale, CA (Sunnyvale - 2 Positions ... Artificial Intelligence: 2-5 years Machine Learning: 2-5 years of experience in building or ...

TWC Business Unit

Software Engineer (Austin)

Austin, TX · On-site

$6.0K - $7.0K/mo

... database machine learning tools (e.g., BigQuery ML) to analyze and model large-scale datasets ... those with H1B visas or those currently on student or postgraduate visas. In compliance with ...

All JobsH1B Machine Learning JobsH1B Machine Learning Jobs in Austin, TX

H1B Machine Learning information

See Austin, TX salary details

$25.3K

$42.2K

$87.2K

How much do h1b machine learning jobs pay per year?

As of Aug 20, 2026, the average yearly pay for h1b machine learning in Austin, TX is $42,209.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,200.00 and $45,600.00 per year, depending on experience, location, and employer.

What is an H1B machine learning job?

An H1B Machine Learning job refers to a position in the field of machine learning offered by a U.S. employer to a foreign worker who is authorized to work in the United States under the H1B visa program. These jobs typically involve designing, developing, and deploying algorithms that enable computers to learn from data. H1B Machine Learning professionals may work on projects such as natural language processing, computer vision, or predictive analytics. Employers must sponsor the H1B visa, and the worker must have specialized knowledge and at least a bachelor's degree in a related field.

What are the key skills and qualifications needed to thrive as a machine learning engineer on an H1B visa?

To thrive as a Machine Learning Engineer on an H1B visa, you generally need a strong background in computer science, mathematics, and statistics, typically demonstrated by a relevant degree and practical experience. Proficiency in programming languages like Python or R, experience with ML frameworks such as TensorFlow or PyTorch, and familiarity with cloud platforms are essential, while certifications like AWS Certified Machine Learning may be advantageous. Strong problem-solving abilities, effective communication, and adaptability help you collaborate across diverse teams and rapidly evolving projects. These skills and qualifications are vital for delivering impactful ML solutions and meeting the rigorous expectations of U.S. employers sponsoring H1B visas.

What are some common challenges H1B machine learning professionals face when adapting to a new work environment in the U.S.?

H1B Machine Learning professionals often encounter challenges such as adjusting to new workplace cultures, navigating different communication styles, and understanding expectations for collaboration and project ownership. Additionally, they may need to quickly familiarize themselves with the company’s tech stack and agile workflows while maintaining compliance with visa-related documentation. Building strong relationships with colleagues and proactively seeking mentorship can help ease the transition and accelerate professional growth.

What is the difference between H1B Machine Learning vs H1B Data Scientist?

AspectH1B Machine LearningH1B Data Scientist
Required CredentialsDegree in CS, ML, or related field; certifications like TensorFlow, AWSDegree in CS, Statistics, or related; certifications in data analysis tools
Work EnvironmentResearch labs, tech companies, startups focusing on ML modelsBusiness analytics, data analysis teams, consulting firms
Employer & Industry UsageTech firms, AI startups, R&D departmentsFinance, healthcare, e-commerce, consulting
Common Search & ComparisonYesYes

H1B Machine Learning and H1B Data Scientist roles often overlap in credentials and industry usage. Machine Learning specialists focus on developing algorithms and models, while Data Scientists analyze data to derive insights. Both roles are in high demand across tech and industry sectors, but their core responsibilities differ slightly, making them distinct yet related career paths.

What are popular job titles related to H1B Machine Learning jobs in Austin, TX?

For H1B Machine Learning jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching H1B Machine Learning jobs in Austin, TX look for?

The top searched job categories for H1B Machine Learning jobs in Austin, TX are:

What cities near Austin, TX are hiring for H1B Machine Learning jobs?

Cities near Austin, TX with the most H1B Machine Learning job openings:

H1B Machine Learning jobs near you
Infographic showing various H1B Machine Learning job openings in Austin, TX as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $42,209 per year, or $20.3 per hour.

Artificial Intelligence / Machine Learning Consultant for Texas DIR, Austin, Tx

Pedigo Staffing Services

Austin, TX • On-site

$120 - $180/hr

Other

Re-posted 4 days ago

Job description

Artificial Intelligence / Machine Learning Consultant for Texas DIR, Austin, Tx
  • Austin, TX

Title: Artificial Intelligence / Machine Learning Consultant

Agency: Texas Department of Information Resources

Location: North Austin, Texas 78758

Solicitation: RFR041FY26

Duration: On-going, possibly four years

Contract Type: W2 with benefits

Visa requirements: US Citizen, Greencard Holder, EAD. No H1B

Telework Policy: Client site and telework hybrid

Required/Preferred Skill Sets:

  • 8 years, Required - Hands-on software engineering experience.
  • 8 years, Required - Expertise in modern cloud platforms.
  • 8 years, Required - trong proficiency in: TypeScript/JavaScript, Python, or C#; Modern UI frameworks (React, Angular, Web Components).
  • 8 years, Required - Experience with integrating APIs (LLMs, internal services, data platforms).
  • 8 years, Required - Experience with CI/CD platforms using GitHub Actions, Azure DevOps, or equivalent including building and deploying applications.
  • 8 years, Required - Experience with infrastructure as code and automating environments (e.g., Terraform, ARM/Bicep, or similar tools. Experience working directly with customers or frontline operational teams to build and improve solutions.
  • 8 years, Required - Extend tools like Salesforce, Appian, ServiceNow, etc. Demonstrated success delivering systems end to end from design to deploy.
  • 8 years, Required - Understanding of security frameworks (NIST, Zero Trust, TX-RAMP expectations).
  • 8 years, Required - Excellent communication and cross-functional collaboration skills.
  • 8 years, Required - Ability to decide when NOT to use low-code.
  • 8 years, Required - Ability to identify high-value use cases and ability to observe workflows.
  • 8 years, Required - Bachelor’s degree in Computer Science, Engineering, or related field OR Equivalent experience (10+ years) in hands-on modern engineering roles.
  • 8 years, Preferred - Experience in state government, regulated environments, or multi-agency integration projects.
  • 8 years, Preferred - Prior FDE or technical field engineering experience at a software platform company.
  • 8 years, Preferred - Experience designing, evaluating, or implementing AI-enabled workflows using commercial, open-source, or government-approved LLM platforms, including patterns such as retrieval-augmented generation, agentic workflows, model evaluation...cont. next line...
  • 8 years, Preferred - prompt management, human-in-the-loop review, and responsible AI controls. Experience with shared technical services or modernization programs (e.g., TSS/MSI) .
  • 8 years, Preferred - Experience producing reusable components, design systems, developer tooling.
  • 8 years, Preferred - Ability to compare AI/LLM options using objective criteria such as data sensitivity, hosting model, latency, cost, accuracy, explainability, auditability, security controls, integration complexity, and operational sustainability.
  • 8 years, Preferred - CISSP, CCSP, or CISM
  • 8 years, Preferred - Kubernetes certifications (CKA/CKAD)
  • 8 years, Preferred - TOGAF or architecture certifications
  • 8 years, Preferred - Scrum Master or SAFe Agile certs
  • 6 years, Preferred - TX-RAMP knowledge or auditor training
  • 1 years, Preferred - Cloud architecture, DevOps, AI, security, or Kubernetes certifications from one or more major providers, such as Azure, AWS, Google Cloud, Kubernetes, HashiCorp, ISC2, ISACA, or equivalent.

The Forward Deployed Engineer (FDE) works directly with DIR and partner agencies to rapidly design, build, deploy, and iterate modern digital solutions—often working onsite or embedded with mission teams.

  • FDE bridges gaps between product teams, security, business units, and cloud engineering
  • FDE should apply platform-agnostic engineering practices and evaluate AI/LLM capabilities based on business need, security requirements, data classification, interoperability, sustainability, and total cost of ownership rather than defaulting to a single cloud, model, or vendor ecosystem.
  • Provides FDE methodology and best practices to DIR staff for knowledge transfer sessions and skill growth. Supports IT and other AI initiative at DIR.

This role is intended to bring advanced, forward-looking technical capability to DIR and partner agencies while remaining flexible, platform-agnostic, and outcomes-focused.

  • The consultant should be able to work at the intersection of modern software engineering, cloud-native architecture, AI-enabled development, automation, security, and agency mission delivery.
  • Rather than prescribing a specific cloud platform, LLM provider, or toolchain, the role should emphasize the ability to evaluate technologies based on business need, security posture, data sensitivity, interoperability, cost, operational maturity, and long-term sustainability.
  • The ideal candidate should help DIR and agencies understand what is possible with modern technology, translate emerging capabilities into practical delivery patterns, and coach internal teams on how to adopt those capabilities responsibly.
  • This includes helping teams turn ambiguous problems into practical, AI-enabled workflows, while exploring AI, automation, APIs, integration patterns, DevSecOps, and reusable components.
  • Focus on rapid prototyping and delivering value without assuming any single vendor or solution is always the right fit.
  • The goal is to raise technical fluency, accelerate modernization, and build internal capability while preserving architectural flexibility.
  • The role should be aspirational in terms of skill level and innovation, but not overly prescriptive in terms of specific products, platforms, or implementation methods.

Deliverables

  • Production-ready code, pipelines, infrastructure templates, and documentation.
  • Architecture diagrams, operational runbooks, and security compliance mappings.
  • AI-assisted development workflows and accelerators.
  • Knowledge transfer sessions and training for agency development staff.

Key Responsibilities

  • Deliver high-quality application, Application Programming Interface (API), Model Context Protocol (MCP), and automation components using cloud-native architectures.
  • Develop rapid prototypes, pilots, and production systems using modern engineering patterns.
  • Integrate systems across agencies using secure, scalable, human-in-the-loop workflows.
  • Implement DevSecOps automation (CI/CD, IaC, container orchestration, cloud pipelines).
  • Collaborate directly with agency stakeholders to gather requirements and convert them into working software.
  • Deploy AI-enabled development workflows and LLM-assisted capabilities.
  • Troubleshoot complex production issues and lead root-cause analysis.
  • Mentor agency developers, maturing internal capability and reducing vendor reliance.
  • Provide documentation, architectural guidance, and knowledge transfer.
  • Rapidly build AI-powered tools using existing systems, and create new applications where needed, to move from experimentation to real impact.
  • Comfort working across cloud environments and internal enterprise systems.
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