Senior ML Engineer Visa Sponsorship Jobs in Texas
Senior ML engineer visa sponsorship jobs in Texas are concentrated in Austin, Dallas, and Houston, where companies like Dell Technologies, AT&T, and Amazon have built significant AI and machine learning teams. Texas's fast-growing tech sector, lower cost of living relative to coastal hubs, and no state income tax make it a serious destination for international ML talent.
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Get Access To All JobsCompany Description
Vitol is an energy and commodities company with revenues of $400 billion in 2023; its primary business is the trading and distribution of energy products globally – it trades over seven million barrels per day of crude oil and products and, at any time, has 250 ships transporting its cargoes. Vitol’s clients include national oil companies, multinationals, leading industrial companies and utilities. Founded in Rotterdam in 1966, today Vitol serves clients from some 40 offices worldwide and is invested in energy assets globally including 16mm3 of storage, 480kbpd of refining capacity, and 7,000 service stations. To date, we have committed over $2.5 billion of capital to renewable projects, and are identifying and developing low-carbon opportunities around the world.
This Role is located in Houston, TX - In office 5x a week
Job Description
As our portfolio of work continues to grow, we are looking for an experienced Machine Learning Engineer to join our data science and machine learning team. The individual will work closely with the data and machine learning specialists, software engineers and commercial teams to deliver machine learning models and applications. We work across the trading business, operations, and other support functions; so the individual will need to be comfortable working with a variety of stakeholders and technologies. The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing, exploratory analysis, model selection and tuning, and implementation of production models. The successful candidate will join a team of experienced, collaborative practitioners, who are (pragmatically) solving some of the most challenging and impactful problems the energy industry is facing; as well as pushing the boundaries around the ‘art of the possible’.
Core Responsibilities include:
- Design, develop, and deploy end-to-end machine learning and data science solutions across our wider business activities (including trading, operations, and support functions) - from raw data ingestion through to production-grade models and monitoring
- Drive adoption and development of the firm's internal GenAI chat platform as one of the technical leads, extending its capabilities through new integrations, data connectors, and domain-specific prompt engineering; work closely with trading desks and operational teams to identify high-value use cases, embed the tool into day-to-day workflows, and ensure outputs are robust, and trusted by end users.
- Apply a broad range of modelling techniques - including time-series forecasting, NLP, classification, and generative AI - to commodity pricing, supply/demand signals, trade flow analysis, and operational optimization problems
- Own the full data science lifecycle on assigned projects: data sourcing and cleaning, exploratory analysis, feature engineering, model selection and validation, deployment, and ongoing performance monitoring
- Build and maintain robust, well-tested, production-quality code; contribute to shared infrastructure including ML pipelines, data orchestration, and model serving layers
- Integrate ML and GenAI outputs into existing trading systems, dashboards, and workflows; work with software engineers to ensure reliable, scalable adoption across the business
- Communicate analytical findings and model outputs clearly to non-technical stakeholders; present results, assumptions, and limitations in a manner that supports confident commercial decision-making
- Actively participate in code reviews, experiment design, and tooling decisions; mentor colleagues and help raise the overall standard of analytical and engineering practice across the team
Qualifications
- Master’s degree or equivalent in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field
- Fluency in Python for both data science and engineering purposes: clean, modular, well-documented code, with strong understanding of software engineering best practices including version control, testing, and code review
- 5+ years of industry experience developing and deploying machine learning or statistical models, with a proven track record of delivering end-to-end solutions in production environments
- Demonstrable experience applying a broad range of ML methodologies (supervised and unsupervised learning, time-series modelling, NLP/LLMs, optimization) to real-world business problems
- Strong proficiency with ML frameworks (e.g. PyTorch, scikit-learn, Transformers) and experience building or consuming LLM-based pipelines and GenAI applications
- Experience with cloud platforms (AWS preferred) and modern MLOps practices: containerization (Docker/Kubernetes), CI/CD, data pipeline orchestration (e.g. Airflow, Dagster), and model serving
- Strong analytical and problem-solving ability: capable of defining and scoping open-ended problems, proposing sound methodological approaches, and defending modelling choices with rigorous reasoning
- Excellent written and verbal communication skills, with the confidence to present model outputs, caveats, and commercial implications clearly to non-technical audiences including traders and senior management
- Genuine intellectual curiosity about commodities markets, global energy flows, and the commercial dynamics of trading; willingness to develop domain knowledge as part of the role
Desirable Experience
- Experience in the energy or commodities trading industry, with knowledge of financial markets and trading concepts
- Experience surfacing ML outputs through interactive tools (e.g. Dash, Streamlit, or similar) and presenting use cases to non-technical audiences, including traders and senior management
- Time-series modelling in a trading or financial context, including both ML-based and econometric approaches (e.g. ARIMA, cointegration, regime-switching models)
- Data orchestrators (Airflow, Dagster) and cloud-based ETL/ELT pipelines
Additional Information
Personal Characteristics
- A self-motivated individual who thrives on seeing the results of their work make an impact in the business
- Pragmatic and delivery-focused: comfortable navigating ambiguity, balancing rigor with speed, and making sound judgements under uncertainty
- Methodical and detail-oriented: rigorous in experimental design, data validation, and code quality, with a disciplined approach to documenting assumptions and results
- Resourceful, able to think creatively and adapt in a dynamic environment
- Team player, with an open non-political style and a high level of integrity
- Desire to be a thought-partner in a fast-growing team, and make an impact at a business that sits at the heart of the world’s energy flows
Work Environment
This job operates in a professional office environment. Because of the collaborative, fast-paced, and high energy nature of our business, Vitol requires team members to work from our fully-equipped office.
What We Offer
- Competitive salary and benefits package
- Large diversity of projects with real-world impacts on a truly global scale
- Entrepreneurial environment within a flat hierarchy, where great ideas come to life quickly
- Close collaboration with various business units across our key regions (eg. London, Singapore, Houston, Geneva)
- A highly motivated DS and ML team comprised of experienced individuals with a supportive attitude and great team spirit
- Being part of the energy transition through increased emphasis on renewable & alternative energy sources at a pivotal moment in the industry
- Strong management commitment to incorporating machine learning into the future of Vitol’s operations
All your information will be kept confidential according to EEO guidelines.
Company Description
Vitol is an energy and commodities company with revenues of $400 billion in 2023; its primary business is the trading and distribution of energy products globally – it trades over seven million barrels per day of crude oil and products and, at any time, has 250 ships transporting its cargoes. Vitol’s clients include national oil companies, multinationals, leading industrial companies and utilities. Founded in Rotterdam in 1966, today Vitol serves clients from some 40 offices worldwide and is invested in energy assets globally including 16mm3 of storage, 480kbpd of refining capacity, and 7,000 service stations. To date, we have committed over $2.5 billion of capital to renewable projects, and are identifying and developing low-carbon opportunities around the world.
This Role is located in Houston, TX - In office 5x a week
Job Description
As our portfolio of work continues to grow, we are looking for an experienced Machine Learning Engineer to join our data science and machine learning team. The individual will work closely with the data and machine learning specialists, software engineers and commercial teams to deliver machine learning models and applications. We work across the trading business, operations, and other support functions; so the individual will need to be comfortable working with a variety of stakeholders and technologies. The Machine Learning Engineer at Vitol has visibility and impact across the full project workflow: from working with business stakeholders to help define the project, to data collation and processing, exploratory analysis, model selection and tuning, and implementation of production models. The successful candidate will join a team of experienced, collaborative practitioners, who are (pragmatically) solving some of the most challenging and impactful problems the energy industry is facing; as well as pushing the boundaries around the ‘art of the possible’.
Core Responsibilities include:
- Design, develop, and deploy end-to-end machine learning and data science solutions across our wider business activities (including trading, operations, and support functions) - from raw data ingestion through to production-grade models and monitoring
- Drive adoption and development of the firm's internal GenAI chat platform as one of the technical leads, extending its capabilities through new integrations, data connectors, and domain-specific prompt engineering; work closely with trading desks and operational teams to identify high-value use cases, embed the tool into day-to-day workflows, and ensure outputs are robust, and trusted by end users.
- Apply a broad range of modelling techniques - including time-series forecasting, NLP, classification, and generative AI - to commodity pricing, supply/demand signals, trade flow analysis, and operational optimization problems
- Own the full data science lifecycle on assigned projects: data sourcing and cleaning, exploratory analysis, feature engineering, model selection and validation, deployment, and ongoing performance monitoring
- Build and maintain robust, well-tested, production-quality code; contribute to shared infrastructure including ML pipelines, data orchestration, and model serving layers
- Integrate ML and GenAI outputs into existing trading systems, dashboards, and workflows; work with software engineers to ensure reliable, scalable adoption across the business
- Communicate analytical findings and model outputs clearly to non-technical stakeholders; present results, assumptions, and limitations in a manner that supports confident commercial decision-making
- Actively participate in code reviews, experiment design, and tooling decisions; mentor colleagues and help raise the overall standard of analytical and engineering practice across the team
Qualifications
- Master’s degree or equivalent in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field
- Fluency in Python for both data science and engineering purposes: clean, modular, well-documented code, with strong understanding of software engineering best practices including version control, testing, and code review
- 5+ years of industry experience developing and deploying machine learning or statistical models, with a proven track record of delivering end-to-end solutions in production environments
- Demonstrable experience applying a broad range of ML methodologies (supervised and unsupervised learning, time-series modelling, NLP/LLMs, optimization) to real-world business problems
- Strong proficiency with ML frameworks (e.g. PyTorch, scikit-learn, Transformers) and experience building or consuming LLM-based pipelines and GenAI applications
- Experience with cloud platforms (AWS preferred) and modern MLOps practices: containerization (Docker/Kubernetes), CI/CD, data pipeline orchestration (e.g. Airflow, Dagster), and model serving
- Strong analytical and problem-solving ability: capable of defining and scoping open-ended problems, proposing sound methodological approaches, and defending modelling choices with rigorous reasoning
- Excellent written and verbal communication skills, with the confidence to present model outputs, caveats, and commercial implications clearly to non-technical audiences including traders and senior management
- Genuine intellectual curiosity about commodities markets, global energy flows, and the commercial dynamics of trading; willingness to develop domain knowledge as part of the role
Desirable Experience
- Experience in the energy or commodities trading industry, with knowledge of financial markets and trading concepts
- Experience surfacing ML outputs through interactive tools (e.g. Dash, Streamlit, or similar) and presenting use cases to non-technical audiences, including traders and senior management
- Time-series modelling in a trading or financial context, including both ML-based and econometric approaches (e.g. ARIMA, cointegration, regime-switching models)
- Data orchestrators (Airflow, Dagster) and cloud-based ETL/ELT pipelines
Additional Information
Personal Characteristics
- A self-motivated individual who thrives on seeing the results of their work make an impact in the business
- Pragmatic and delivery-focused: comfortable navigating ambiguity, balancing rigor with speed, and making sound judgements under uncertainty
- Methodical and detail-oriented: rigorous in experimental design, data validation, and code quality, with a disciplined approach to documenting assumptions and results
- Resourceful, able to think creatively and adapt in a dynamic environment
- Team player, with an open non-political style and a high level of integrity
- Desire to be a thought-partner in a fast-growing team, and make an impact at a business that sits at the heart of the world’s energy flows
Work Environment
This job operates in a professional office environment. Because of the collaborative, fast-paced, and high energy nature of our business, Vitol requires team members to work from our fully-equipped office.
What We Offer
- Competitive salary and benefits package
- Large diversity of projects with real-world impacts on a truly global scale
- Entrepreneurial environment within a flat hierarchy, where great ideas come to life quickly
- Close collaboration with various business units across our key regions (eg. London, Singapore, Houston, Geneva)
- A highly motivated DS and ML team comprised of experienced individuals with a supportive attitude and great team spirit
- Being part of the energy transition through increased emphasis on renewable & alternative energy sources at a pivotal moment in the industry
- Strong management commitment to incorporating machine learning into the future of Vitol’s operations
All your information will be kept confidential according to EEO guidelines.
Senior ML Engineer Job Roles in Texas
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Search Senior ML Engineer Jobs in TexasSenior ML Engineer Jobs in Texas: Frequently Asked Questions
Which companies in Texas sponsor visas for senior ML engineers?
Texas has a strong base of employers with established visa sponsorship programs for senior ML engineers. Technology firms like Dell Technologies, Texas Instruments, and Oracle have Austin and Dallas offices that regularly file H-1B petitions for ML roles. National employers with major Texas footprints, including Amazon, Google, and Apple (all with Austin campuses), also sponsor senior ML engineering positions. Defense and energy contractors in the Dallas-Fort Worth and Houston areas are additional sponsoring employers worth researching.
Which visa types are most common for senior ML engineer roles in Texas?
The H-1B is the most common visa category for senior ML engineers in Texas, as the role consistently qualifies as a specialty occupation requiring a bachelor's degree or higher in computer science, mathematics, or a related field. Candidates with extraordinary recognition in machine learning research may qualify for the O-1A. Those transferring within a multinational company may be eligible for the L-1B for specialized knowledge workers. Australian citizens can also explore the E-3 visa, which has no lottery.
Which cities in Texas have the most senior ML engineer sponsorship jobs?
Austin leads Texas for senior ML engineer sponsorship activity, driven by its dense concentration of tech employers and a strong University of Texas pipeline. Dallas-Fort Worth follows closely, with major corporate headquarters in financial services, telecom, and enterprise software all fielding ML teams. Houston has a growing presence, particularly in energy tech and healthcare AI, with employers applying machine learning to data-heavy industries. San Antonio has a smaller but emerging cybersecurity and defense ML cluster.
How to find senior ml engineer visa sponsorship jobs in Texas?
Migrate Mate filters job listings specifically by visa sponsorship availability, making it straightforward to browse senior ML engineer roles in Texas without sorting through positions that don't sponsor. You can narrow results by city, including Austin, Dallas, and Houston, to focus on the markets with the highest sponsorship activity. Because Migrate Mate focuses on international candidates, the listings are curated for those who need an employer willing to file an H-1B or similar petition.
Are there state-specific factors that affect visa sponsorship for senior ML engineers in Texas?
Texas has no state income tax, which affects how employers structure compensation benchmarks and how prevailing wage calculations compare to states like California or New York. The Department of Labor's prevailing wage determinations for senior ML engineers are based on the specific metropolitan statistical area, so Austin, Dallas, and Houston each have different wage floors that employers must meet when filing an H-1B Labor Condition Application. Texas also benefits from several top research universities, including UT Austin and Rice University, that feed into local ML hiring pipelines and sometimes facilitate OPT-to-sponsorship transitions.
What is the prevailing wage for sponsored senior ml 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.
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