What are the responsibilities and job description for the Machine Learning Engineer, Data & Insights, Surface & HSE position at Chevron?
Chevron is accepting online applications for the position Machine Learning Engineer, Data & Insights, Surface & HSE through June15th, 2025 at 11:59 p.m. (CST)
Overview
Chevron is seeking a Machine Learning Engineer to transform AI and data science concepts into scalable, production-grade solutions. You will build, deploy, and maintain machine learning systems that operate reliably at enterprise scale. Working alongside data scientists, software engineers, and cross-functional partners, you will bridge the gap between research and production to deliver AI systems aligned with strategic business objectives. Your work will drive smarter decisions and measurable outcomes across the organization, with strong emphasis on enterprise data platforms, AI-enabled transformation, and real-time analytics in complex domains such as Upstream (Surface) and Health Safety and Environment (HSE).
Responsibilities for this position may include but are not limited to:
Solution Design & Development
Identify data sources, technology stacks, and design patterns to address business challenges using AI and ML, with emphasis on Azure-based data platforms and enterprise architectures.
Partner with Data Scientists, Data Engineers, and IT teams to integrate models into enterprise data pipelines and large-scale data ecosystems.
Design scalable data and AI solutions enabling near real-time analytics and cross-domain data integration.
Model Operationalization
Transform prototypes into scalable, production-ready solutions across distributed and cloud-native environments.
Design and execute experiments to fine-tune algorithms for performance, latency, and resource efficiency, aligned with enterprise-scale workloads.
Configure and manage infrastructure for low-latency, highly available, and resilient ML workloads integrated with enterprise data platforms.
Deployment & Integration
Build, maintain, and optimize CI/CD pipelines for automated AI/ML deployments using modern DevOps and automation tooling.
Integrate models with enterprise MLOps infrastructure, APIs, and downstream business applications across multiple domains.
Leverage automation tools to operationalize workflows and improve delivery consistency.
Monitoring & Maintenance
Implement comprehensive monitoring, alerting, and exception-handling systems for deployed models and data pipelines.
Collaborate with Data Scientists and business stakeholders to ensure inference outputs drive accurate, consistent, and high-value decisions.
Proactively identify and resolve model drift, performance degradation, data quality issues, and system integration challenges.
Required Qualifications
Relocation is not offered for this role. Only local candidates will 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.
U.S. Regulatory Notice
Chevron is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, sex (including pregnancy), sexual orientation, gender identity, gender expression, national origin or ancestry, age, mental or physical disability, medical condition, reproductive health decision-making, military or veteran status, political preference, marital status, citizenship, genetic information or other characteristics protected by applicable law.
Overview
Chevron is seeking a Machine Learning Engineer to transform AI and data science concepts into scalable, production-grade solutions. You will build, deploy, and maintain machine learning systems that operate reliably at enterprise scale. Working alongside data scientists, software engineers, and cross-functional partners, you will bridge the gap between research and production to deliver AI systems aligned with strategic business objectives. Your work will drive smarter decisions and measurable outcomes across the organization, with strong emphasis on enterprise data platforms, AI-enabled transformation, and real-time analytics in complex domains such as Upstream (Surface) and Health Safety and Environment (HSE).
Responsibilities for this position may include but are not limited to:
Solution Design & Development
Identify data sources, technology stacks, and design patterns to address business challenges using AI and ML, with emphasis on Azure-based data platforms and enterprise architectures.
Partner with Data Scientists, Data Engineers, and IT teams to integrate models into enterprise data pipelines and large-scale data ecosystems.
Design scalable data and AI solutions enabling near real-time analytics and cross-domain data integration.
Model Operationalization
Transform prototypes into scalable, production-ready solutions across distributed and cloud-native environments.
Design and execute experiments to fine-tune algorithms for performance, latency, and resource efficiency, aligned with enterprise-scale workloads.
Configure and manage infrastructure for low-latency, highly available, and resilient ML workloads integrated with enterprise data platforms.
Deployment & Integration
Build, maintain, and optimize CI/CD pipelines for automated AI/ML deployments using modern DevOps and automation tooling.
Integrate models with enterprise MLOps infrastructure, APIs, and downstream business applications across multiple domains.
Leverage automation tools to operationalize workflows and improve delivery consistency.
Monitoring & Maintenance
Implement comprehensive monitoring, alerting, and exception-handling systems for deployed models and data pipelines.
Collaborate with Data Scientists and business stakeholders to ensure inference outputs drive accurate, consistent, and high-value decisions.
Proactively identify and resolve model drift, performance degradation, data quality issues, and system integration challenges.
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, with strong proficiency in Python.
- Proven track record of deploying machine learning models and/or 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.
- 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 real-time analytics platforms.
- Deep understanding of model lifecycle management, performance optimization, and ML system design patterns in enterprise environments.
- Domain experience in Oil & Gas, including Surface, Subsurface, Wells and HSE
- Experience enabling AI adoption, defining enterprise roadmaps, and delivering measurable business value through data and AI solutions.
Relocation is not offered for this role. Only local candidates will 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.
U.S. Regulatory Notice
Chevron is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, sex (including pregnancy), sexual orientation, gender identity, gender expression, national origin or ancestry, age, mental or physical disability, medical condition, reproductive health decision-making, military or veteran status, political preference, marital status, citizenship, genetic information or other characteristics protected by applicable law.