Internship Full Stack Machine Learning Engineer Jobs in Houston, TX

Job Summary:
Vitol is an energy and commodities company with a significant global presence, trading energy products and developing low-carbon opportunities. They are seeking an experienced Machine Learning Engineer to join their data science and machine learning team, responsible for delivering machine learning models and applications across various business functions.
Responsibilities:
• 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:
Required:
• 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
Preferred:
• 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
Company:
The Vitol Group is an energy and commodity trading company involved in exploration, production, refining, terminals, trading, marketing. Founded in 1966, the company is headquartered in New York, USA, with a team of 1001-5000 employees. The company is currently Late Stage.