Interested in this AI/ML Engineer role at Phillips 66?
Apply Now →Skills & Technologies
About This Role
Phillips 66 \& YOU \- Together we can fuel the future
We are seeking a Lead Engineer, Data \& AI Governance , to serve as a technical lead and subject matter expert supporting the development, implementation, and continuous improvement of enterprise data and AI governance capabilities. This role combines hands\-on technical expertise with the ability to lead delivery efforts, guide engineering teams, and collaborate with business and technical stakeholders to establish trusted, well\-governed data and AI assets across the enterprise. The successful candidate will help translate enterprise governance strategies into scalable technical solutions and operational processes.
What You’ll Do
Collaborate with business and technical stakeholders to translate complex requirements into scalable data governance and platform solutions.
Ensure alignment with organizational goals and enterprise data strategy.
Lead and contribute to the design and implementation of enterprise data governance frameworks, including:
Data ownership and stewardship
Access controls and security
Metadata management and lineage
Data quality and certification
Drive adoption of governance processes through stakeholder engagement, training, and change management initiatives.
Support compliance efforts and continuous improvement of governance capabilities.
Contribute to the implementation of data architecture practices that transform raw and operational data into meaningful business insights.
Enhance data discoverability, usability, and accessibility across the enterprise.
Design and implement governance capabilities within platforms such as Databricks Unity Catalog.
Establish and maintain standards for data organization, naming conventions, and access management.
Build and manage metadata structures to support discoverability and governance.
Support advanced data analysis, reporting, and modeling to enable business decision\-making and insight generation.
Identify opportunities for innovation and evaluate emerging technologies and trends.
Recommend and implement enhancements to improve data quality, security, and platform performance.
Lead or contribute to cross\-functional projects and manage competing priorities effectively.
Support the development of data\-driven and AI\-enabled solutions.
Promote safe, ethical, and responsible use of data and AI technologies.
What You’ll Bring – Required
Legally authorized to work in the job posting country.
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
6\+ years of experience in data governance, data management, data architecture, data engineering, or related disciplines.
3\+ years’ experience implementing governance capabilities within enterprise data platforms.
3\+ years of hands\-on experience with Databricks, Unity Catalog, Genie, Python, and SQL.
1\+ years’ experience working with knowledge graphs, semantic models, business glossaries, ontologies, or related contextualization technologies.
What Makes You Stand Out – Preferred
Master’s degree in Computer Science, Engineering, Information Systems, or a related field
Background in energy, manufacturing, logistics, or other asset\-intensive industries
Experience implementing data governance programs within large or regulated organizations.
Experience with machine learning lifecycle management or MLOps practices
Familiarity with AI governance and responsible AI practices.
Hands\-on experience with cloud platforms such as Azure, AWS, or GCP
Familiarity with Agile delivery, Azure DevOps, or CI/CD methodologies
Experience with full\-stack or end\-to\-end data and application development
Experience supporting AI\-enabled data tools or prompt engineering best practices
Compensation Range
This position has a base salary range of $160,200 – $195,800\.
At Phillips 66, we are committed to pay transparency and competitive, equitable compensation. Each role is assigned a salary grade with a defined pay range, benchmarked against industry peers. Where a candidate offer falls within the posted range depends on the candidate's experience, skills, and alignment with the role’s requirements. Offers are made to ensure internal equity and market competitiveness. Our compensation programs are designed to reward performance and support career growth.
Total Rewards
At Phillips 66, providing access to high quality programs and care for you and your family is important to us. Maintaining a culture of well\-being — physical, emotional, social, and financial — is essential for a high\-performing organization. When we are at our best, we are poised to deliver exceptional results — personally and professionally. Benefits for certain eligible, full\-time employees include:
Annual Variable Cash Incentive Program (VCIP) bonus
8% 401k company match
Cash Balance Account pension
Medical, Dental, and Vision benefits with an annual company contribution to a Health Savings Account for employees on HDHP
Total well\-being programs and incentives, including Employee Assistance Plan, well\-being reimbursement, and backup family care services
Learn more about Phillips 66 Total Rewards (http://hr.phillips66\.com) .
Phillips 66 has more than 140 years of experience in providing the energy that enables people to dream bigger and go farther, faster. We are committed to improving lives, and that is our promise to our employees and our communities. We are sustained by the backgrounds and experiences of our diverse teams, which reflect who we are, the environment we create and how we work together. We have been recognized by the Human Rights Campaign, U.S. Department of Labor and the Military Times for our continued commitment to inclusive practices and policies in the hiring and retention of those in the LGBTQ\+ community and military veterans. Our company is built on values of safety, honor and commitment. We call our cultural mindset Our Energy in Action, which we define through four simple, intuitive behaviors: We work for the greater good, cultivate an environment of trust, seek different perspectives and pursue excellence.
Learn more about Phillips 66 and how we are working to meet the world's energy needs today and tomorrow, by visiting phillips66\.com.
To be considered
In order to be considered for this position you must complete the entire application process, which includes answering all prescreening questions and providing your eSignature on or before the requisition closing date of 8/4/2026\.
Candidates for regular U.S. positions must be a U.S. citizen or national, or an alien admitted as permanent resident, refugee, asylee or temporary resident under 8 U.S.C. 1160(a) or 1255(a)(1\). Individuals with temporary visas such as E, F\-1, H\-1, H\-2, L, B, J, or TN or who need sponsorship for work authorization now or in the future, are not eligible for hire.
Phillips 66 is an Equal Opportunity Employer
Salary Context
This $160K-$195K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Phillips 66, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($178K) sits 19% below the category median. Disclosed range: $160K to $195K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Phillips 66 AI Hiring
Phillips 66 has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US. Compensation range: $195K - $195K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.