Interested in this AI/ML Engineer role at ONEOK?
Apply Now →Skills & Technologies
About This Role
\#WeAreONEOK – Fortune 500 company. 100\+ years in business. Leading midstream service provider. Safety first. Sustainable operations. Environmentally responsible. Employee focused.
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
JOB SUMMARY
Job Profile Summary/Mission
Design, build, and operate the AI systems that Operations relies on. The AI Engineer is responsible for the most technically demanding work — advanced algorithms, production agents, and shared frameworks — and delivers reusable infrastructure that enables the rest of the team to work efficiently.
Essential Functions and Core Responsibilities
- Production AI systems: Develop, deploy, and maintain operations\-critical AI systems, including agents, harnesses, tools, and evaluation pipelines. Accountable for reliability, latency, cost, and accuracy.
- Advanced algorithms: Develop numerical optimization, forecasting, computer vision, and anomaly detection capabilities in cases where off\-the\-shelf AI does not meet the operational requirement.
- Shared frameworks: Develop the code templates, agent skills, and function\-calling patterns reused by Forward Deployed Engineers and AI Analysts, enabling consistent and efficient delivery across the team.
- Tooling and prompts: Design, tune, and evaluate purpose\-built tools, function calls, and prompt templates.
- Operations\-aware user experience: Develop custom user interfaces for operations workflows, designed from user requirements.
- Mentorship: Develop the capabilities of Forward Deployed Engineers and AI Analysts in AI engineering practices, code quality, and system design. Establish and maintain the technical standard for the team.
- Security and IT partnership: Ensure AI systems meet ONEOK IT and security standards as they are transitioned to production.
Education
- Bachelor's degree in Engineering, Management Information Systems, Computer Sciences, Accounting, Finance, Business Administration, or other related field with specific job\-related experience as follows:
Required Experience
- Minimum 5 years of software engineering experience, including demonstrated production ownership of AI or ML systems.
- Extensive experience with LLM\-based systems, including agent frameworks, tool use, evaluations, prompt engineering, and retrieval.
- Strong Python engineering skills, with familiarity in at least one of optimization, forecasting, or computer vision.
- Experience designing APIs, SDKs, or frameworks consumed by other engineers.
- Proven track record operating systems in production, including monitoring, incident response, and cost management.
How We Measure Success
--------------------------
- Reliability, latency, and unit cost of production AI systems.
- Adoption of shared frameworks and templates across Forward Deployed Engineers and AI Analysts, measured by active reuse.
- Evaluation coverage and regression detection rate on deployed systems.
- Quality of mentorship, reflected in the growth and independence of Forward Deployed Engineers and AI Analysts.
Team \& Collaboration
Engages across the full Operations Analytics team. Partners with Forward Deployed Engineers to advance proven prototypes into enterprise\-grade products, supports AI Analysts with tooling and prompt optimization, and relies on the Data Steward for reliable, contextualized data. Maintains close working relationships with IT and Security.
Knowledge, Skills and Abilities
- Skills in: relational database design including table structures, indexes and primary/foreign key fields.
- Skills in: use and function of office equipment including computers and applicable software.
- Skills in: interacting, advising and communicating effectively.
- Skills in: developing solutions to complicated issues using abstract thinking in new or complex situations.
- Skills in: managing and prioritizing multiple assignments with competing deadlines.
- Skills in: reading and interpreting correspondence, reports, production statistics, cost analyses, contracts, accounting statements, income statements, ledgers, manuals and legal documents.
- Ability to: develop information, conduct meetings and present information to individuals and groups.
- Ability to: apply math, algebra, statistical and volumetric methods.
- Ability to: be self\-motivated and self\-directed.
- Ability to: influence and facilitate discussions across a wide range of teams and technologies.
- Ability to: mentor less experienced personnel on complex technologies.
Licenses and Certifications
- None required
Strength Factor Rating \- Physical Demands/Requirements
- Sedentary Work \- Exerting up to 10 pounds of force occasionally (Occasionally: activity or condition exists up to 1/3 of the time) and/or a negligible amount of force frequently (Frequently: activity or condition exists from 1/3 to 2/3 of the time) to lift, carry, push, pull, or otherwise move objects, including the human body. Sedentary work involves sitting most of the time, but may involve walking or standing for brief periods of time. Jobs are sedentary if walking and standing are required only occasionally and all other sedentary criteria are met.
Strength Factor Description \- Physical Demands/Requirements
- Standing: Remaining on one's feet in an upright position at a work station without moving about (Occasionally)
- Walking: Moving about on foot (Frequently)
- Sitting: Remaining in a seated position (Constantly)
- Lifting: Raising or lowering an object from one level to another (includes upward pulling) (Occasionally)
- Carrying: Transporting an object, usually holding it in the hands or arms, or on the shoulder (Occasionally)
- Pushing: Exerting force upon an object so that the object moves away from the force (Occasionally)
- Pulling: Exerting force upon an object so that the object moves toward the force (includes jerking) (Occasionally)
- Climbing: Ladders, Stairs (Occasionally)
- Balancing: Maintaining body equilibrium to prevent falling (Occasionally)
- Stooping: Bending the body downward and forward by bending the spine at the waist (Occasionally)
- Kneeling: Bending the legs at the knees to come to rest on the knee or knees (Occasionally)
- Crouching: Bending the body downward and forward by bending the legs and spine (Occasionally)
- Crawling: Moving about on the hands and arms in any direction (Occasionally)
- Reaching: Extending hands and arms in any direction (Constantly)
- Handling: Seizing, holding, grasping, turning or otherwise working with the hand or hands (Manual Dexterity) (Constantly)
- Fingering: Picking, pinching or otherwise working with the fingers primarily (Finger Dexterity) (Constantly)
- Feeling: Perceiving such attributes of objects/materials as size, shape, temperature, texture, movement or pulsation by receptors in the skin, particularly those of the finger tips (Constantly)
- Talking: Expressing or exchanging ideas/information by means of the spoken word (Frequently)
- Hearing: Perceiving the nature of sound by the ear (Frequently)
- Tasting/Smelling: (Occasionally)
- Near Vision: Clarity of vision at 20 inches or less (Constantly)
- Far Vision: Clarity of vision at 20 feet for more (Frequently)
- Depth Perception: Three\-dimensional vision; ability to judge distances and spatial relationships so as to see objects where and as they actually are (Frequently)
- Vision: Color \- The ability to identify and distinguish colors (Constantly)
Working Conditions/Environment
- Employee is subject to inside environmental conditions
Working Conditions
- Well lighted, climate controlled areas (Constantly)
- Frequent repetitive motion (Constantly)
- CRT (Computer Monitor(s)) (Constantly)
Travel
- Travel to other locations required.
Driving
- Based on assigned tasks, employee may be assigned a company vehicle requiring the applicable driver's license
*ONEOK is an equal opportunity employer committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, sex, pregnancy, sexual orientation, age, religion, creed, national origin, gender identity, disability, military/veteran status, genetic information or any other categories protected by applicable law.*
*The job description is not intended to be a complete list of all responsibilities, duties or skills required for the job and is subject to review and change at any time, with or without notice, in accordance with the needs of ONEOK.*
*ONEOK is committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. To request an accommodation email* *HRSolutions@ONEOK.com* *or call 1\-855\-663\-6547\.*
Expected Salary Range
$92,000\.00 \- $138,000\.00
Salary Context
This $92K-$138K range is in the lower quartile 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 ONEOK, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($115K) sits 47% below the category median. Disclosed range: $92K to $138K.
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.
ONEOK AI Hiring
ONEOK has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Tulsa, OK, US. Compensation range: $138K - $138K.
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.