Interested in this AI/ML Engineer role at Halliburton?
Apply Now →Skills & Technologies
About This Role
We are looking for the right people — people who want to innovate, achieve, grow and lead. We attract and retain the best talent by investing in our employees and empowering them to develop themselves and their careers. Experience the challenges, rewards and opportunity of working for one of the world’s largest providers of products and services to the global energy industry.
About Cementing
-------------------
Halliburton Cementing is a global leader in well integrity, zonal isolation, and casing support solutions. From surface to Deepwater operations, our technologies ensure secure well construction, long\-term stability, and environmental protection through precise slurry design, placement, and real\-time downhole monitoring. By combining advanced modeling tools with decades of field expertise, Halliburton delivers cementing performance that defines industry standards for reliability and safety.
About the Role
------------------
The AI/ML Engineer is an early\-career member of a cross\-functional product development team focused on building AI\-driven solutions that support well barrier analysis, engineering design, and digital innovation. Working alongside data scientists, software engineers, automation specialists, and domain experts, this role contributes to the development, deployment, and continuous improvement of machine learning and generative AI applications.
This position provides an opportunity to gain hands\-on experience across the AI development lifecycle, including data engineering, model development, validation, and deployment. The successful candidate will collaborate across disciplines, leverage modern AI tools and technologies, and progressively assume greater technical ownership while contributing to solutions that support Halliburton's global operations.
Key Responsibilities
- Develop, train, evaluate, and deploy machine learning and AI models supporting engineering and operational applications.
- Collect, prepare, cleanse, and organize structured and unstructured data from multiple sources for analytics and model development.
- Develop and maintain data pipelines, datasets, and documentation to support scalable AI solutions.
- Assist with the deployment, monitoring, and continuous improvement of machine learning models and AI\-enabled applications.
- Collaborate with software developers, engineers, automation specialists, and product teams to deliver production\-ready AI solutions.
- Utilize Python, machine learning frameworks, large language models (LLMs), and AI\-assisted development tools to accelerate solution delivery.
- Support model validation, performance monitoring, and continuous optimization using established engineering and data science best practices.
- Contribute to technical documentation, knowledge sharing, and continuous improvement initiatives.
- Stay current on emerging technologies, AI methodologies, and industry best practices to support innovation within the Cementing PSL.
Qualifications
------------------
Required
- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or another STEM discipline.
- Minimum 1 year of experience in data science, machine learning, software development, or a related technical field.
- Experience programming in Python and working with common machine learning or data science libraries.
- Understanding of data preparation, machine learning fundamentals, and model development workflows.
- Strong analytical, problem\-solving, and communication skills.
- Ability to work effectively within cross\-functional, collaborative engineering teams.
Preferred
- Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related STEM discipline.
- Experience with Generative AI, Large Language Models (LLMs), AI agents, or retrieval\-augmented generation (RAG).
- Familiarity with cloud computing platforms, MLOps, model deployment, or containerized applications.
- Experience using Git, CI/CD pipelines, SQL, or modern software development practices.
Candidates with qualifications exceeding the minimum requirements may be considered for the Senior AI/ML Engineer role based on experience, additional qualifications, and business needs.
World Class Benefits
------------------------
At Halliburton, we’re committed to supporting you and your family with a comprehensive and affordable benefits package that covers your physical, emotional, financial, and parental needs — now and in the future. When you join our team, you’ll gain access to a wide range of programs designed to help you thrive at work and at home.
Click here to review a summary of the benefits available once you join.
Core Competencies
---------------------
Machine Learning \| Artificial Intelligence \| Generative AI \| Large Language Models (LLMs) \| Python \| Data Engineering \| Data Modeling \| Model Deployment \| MLOps \| AI\-Assisted Development \| Cloud Computing \| Cross\-Functional Collaboration \| Continuous Learning \| Problem Solving
Halliburton is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.
Location
3000 N. Sam Houston Parkway E., Houston, Texas, 77032, United States
Job Details
Requisition Number: 210279
Experience Level: Experienced Hire
Job Family: Engineering/Science/Technology
Product Service Line: Cementing
Full Time / Part Time: Full\-time
Additional Locations for this position:
Compensation Information
Compensation is competitive and commensurate with experience.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Halliburton, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Halliburton AI Hiring
Halliburton 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% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.