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About This Role
Total Number of Openings
1
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
- 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.
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 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 Options:
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.
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.
We are committed to providing reasonable accommodations for qualified individuals with disabilities. If you need assistance or an accommodation, please email us at [email protected].
Chevron participates in E\-Verify in certain locations as required by law.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Chevron, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Chevron AI Hiring
Chevron has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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