Lead Machine Learning Engineer

Chevron is accepting online applications for the position Machine Learning Engineer, Subsurface and Wells Insights through 07/08, 2026 at 11:59 p.m. (Central Time).  

Chevron is seeking a Machine Learning Engineer to build and scale production AI solutions that drive critical decisions across subsurface and wells operations.

In this role, you will partner with data scientists, software engineers, and domain experts to transform advanced AI/ML models into reliable, enterprise-grade systems. These systems are built on and integrated with enterprise data platforms and systems, enabling scalable, cross-domain use of AI across upstream operations. The resulting solutions are used directly by engineers and geoscientists to improve reservoir understanding, optimize production, and enhance drilling and completions performance.

This is a high-impact role focused on deploying AI at scale. You will bridge the gap between experimentation and production while delivering measurable business outcomes across Chevron’s upstream operations.

Responsibilities for this position may include but are not limited to:

Solution Design & Development

• Design and deliver production-grade machine learning solutions aligned with business workflows and enterprise architecture

• Partner with data scientists, data engineers, and IT teams to integrate models into enterprise data platforms, pipelines, and digital products

• Collaborate with subsurface and wells domain experts to translate business challenges into deployable AI/ML solutions

• Select appropriate data sources, technologies, and design patterns to solve complex problems using AI/ML

• Support integration of ML capabilities into tools used by geoscientists, reservoir engineers, and drilling and production teams

Model Operationalization

• Convert prototypes into reliable, production-ready solutions deployed in distributed and cloud-native environments

• Implement end-to-end MLOps practices, including model versioning, automated retraining, and lifecycle management

• Optimize models for performance, scalability, latency, and cost efficiency

• Configure infrastructure to support resilient and highly available ML workloads

• Ensure solutions meet enterprise standards for security, reliability, and maintainability

Deployment & Integration

• Build and maintain CI/CD pipelines for automated model testing, deployment, and release

• Deploy and manage models using cloud-native tooling such as Azure ML, containerization, and orchestration platforms

• Integrate ML solutions with APIs, enterprise systems, and downstream business applications

• Leverage automation to improve delivery speed, consistency, and reliability

Monitoring & Maintenance

• Implement monitoring, alerting, and observability for deployed models and data pipelines

• Detect and address model drift, data quality issues, and performance degradation

• Partner with stakeholders to ensure model outputs drive accurate, consistent, and high-value decisions

• Troubleshoot complex system and integration challenges across distributed environments

Required Qualifications:

• Bachelor's degree in Engineering, Computer Science, Data Science, or a related technical field.

• Minimum 7 years of hands-on experience in software engineering, ML engineering, or enterprise data platforms

• Strong proficiency in Python with solid software engineering fundamentals including testing, version control, and modular application design.

• Proven track record of deploying machine learning models and enterprise data-driven platforms into production environments at scale.

• Solid understanding of the AI/ML lifecycle, including data preparation, model training, evaluation, deployment, and inference.

• Experience with Azure cloud services, including Azure Machine Learning, data platforms, and enterprise integration patterns.

• Experience building and maintaining CI/CD pipelines and applying DevOps practices for ML systems.

• Strong understanding of data governance principles (e.g., Lineage, MDM) and integration across enterprise systems.

• Demonstrated ability to troubleshoot complex distributed systems and work across cross-functional teams.

Preferred Qualifications:

• Master's or Ph.D. in Engineering, Computer Science, Data Science, or a related field.

• 10+ years of relevant technical and enterprise experience in AI, data platforms, or digital transformation.

• Experience with large-scale enterprise data architectures and complex analytical workloads.

• Deep understanding of model lifecycle management, performance optimization, and ML system design patterns in enterprise environments.

• Experience deploying, operating, monitoring, and optimizing generative AI systems, including agent-based and AI-assisted decision-support solutions in enterprise environments.

• Domain experience in upstream oil & gas, including subsurface, wells, and production.

• Experience enabling AI adoption, defining enterprise roadmaps, and delivering measurable business value through data and AI solutions.

Relocation Options:

Relocation will not be considered.

International Considerations:

Expatriate assignments will not be considered.

Chevron regrets that it is unable to sponsor employment Visas or consider individuals on time-limited Visa status for this position.

Houston, Texas

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