Chevron is accepting online applications for the position Principal Applied Scientist - Enterprise AI through January 20, 2026 at 11:59 p.m. (Central Time).

Join Chevron's Enterprise AI team to lead the design and delivery of industry specific foundation models and agentic AI systems that transform decision making and underpin the strategy for future growth for the company. As a Principal Applied Scientist, you will architect and advance the AI foundation for Chevron's proprietary data and workflows.

This role is ideal for senior scientists who blend deep domain expertise in energy with state-of-the-art ML/GenAI methods. You will also set technical direction, mentor multidisciplinary teams, and build reusable frameworks and analytical assets to accelerate Chevron's AI ambitions at global scale.

Key Responsibilities:

• Architect Industry Foundation Models
  Design domain tailored FMs for energy workflows (e.g., subsurface, production operations, supply & trading), leveraging new and emerging FM architectures and advanced methods such as instruction tuning, LoRA, adapter based finetuning, prompt optimization, and RLHF/RLAIF.

• Fine Tune & Optimize Model Weights
  Own the end to end process for weight initialization, adaptation, quantization, distillation, and evaluation to meet accuracy, latency, and cost targets; establish best practices for versioning, reproducibility, and model cards.

• Develop & Operationalize AI Systems
  Build, test, and deploy production grade models and agentic workflows using AzureML, Azure OpenAI Service, and Databricks (Unity Catalog & Model Serving); maintain flexibility to integrate approved third party platform and/or model endpoints via Azure where appropriate, e.g. DataRobot

• Establish Reusable AI Frameworks
  Create modular, reusable components-feature stores, orchestration pipelines, evaluation harnesses, prompt/agent templates, and retrieval layers-that accelerate delivery across multiple AI applications.

• Technical Leadership & Mentorship
  Provide deep technical guidance to data scientists, AI engineers, and software engineers; lead design reviews, code reviews, and drive adoption of best practices for scalable AI.

• Collaborate Across Disciplines
  Partner with business stakeholders and cross functional engineering delivery teams to translate complex problems into measurable, scalable AI solutions, and to embed models into production systems and agentic workflows.

• Model Lifecycle & Responsible AI
  Work alongside Chevron's Data & Insights Department to define standards for governance, monitoring, drift detection, retraining, and evaluation (offline/online); champion fairness, transparency, safety, and auditability across the model lifecycle.

• Innovation & Thought Leadership
  Lead research and experiments on foundation models, multimodal learning, retrieval augmented generation, and reinforcement learning; publish findings internally and externally where appropriate.

Required Qualifications:

• 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 Qualifications:

• 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.

Relocation Options:

Relocation may 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.

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