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Chevron · 1 week ago

Principal Applied Scientist - Enterprise AI

Houston, Texas, United States of America

Full-time

Onsite

Lead/Staff

10+ years exp

Chevron is a leading energy company, and they are seeking a Principal Applied Scientist to lead the design and delivery of AI systems that transform decision making. The role involves architecting AI foundations, mentoring teams, and collaborating across disciplines to develop scalable AI solutions.

EnergyFossil FuelsGeothermal EnergyManufacturingNatural ResourcesOil and GasRenewable Energy

No H1B

Responsibilities

Architect Industry Foundation Models

Fine Tune & Optimize Model Weights

Develop & Operationalize AI Systems

Establish Reusable AI Frameworks

Technical Leadership & Mentorship

Collaborate Across Disciplines

Model Lifecycle & Responsible AI

Innovation & Thought Leadership

Qualification

Foundation models expertiseAI/ML systems deploymentPython proficiencyAzureML experienceDeep domain understandingMLOps familiarityTechnical leadershipCommunicationCross-functional collaborationMentorship ability

Required

Advanced degree (MS or PhD) in computer science, statistics, mathematics, neurosciences, machine learning, engineering or related quantitative field

10+ years building and deploying enterprise-scale AI/ML systems, including technical leadership of complex initiatives

Demonstrated expertise in foundation models and open-source LLM/SLM ecosystems, including fine-tuning, weight management, and evaluation

Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, Hugging Face)

Hands-on experience with AzureML, Azure OpenAI Service, and Databricks

Deep domain understanding of energy workflows (upstream, downstream, or supply & trading) and the ability to encode domain insights into model design

Excellent communication, cross-functional collaboration, and the ability to set technical strategy and mentor teams

Preferred

Experience with multimodal FMs (text, time-series, tabular, vision) and RAG systems

Experience with agentic AI architectures, generative AI, and reinforcement learning

Familiarity with MLOps, CI/CD for ML, model serving, experiment tracking, and online evaluation

Demonstrated background in optimization methods, causal inference, or simulation/agent-based modelling for decision support

Demonstrated experience in distributed model training on multi-GPU and multi-node clusters and ability to optimize and troubleshoot training across a cluster to ensure efficient utilization of resources and consistent model performance

Record of technical publications, patents, or conference presentations in AI/ML