Interested in this AI Software Engineer role at Halliburton?
Apply Now →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 Landmark
Landmark, a Halliburton business line, provides the industry’s most comprehensive suite of digital solutions for exploration, drilling, and production optimization. Its software and data platforms empower customers to model subsurface assets, manage drilling risk, and accelerate decision-making through cloud, AI, and advanced analytics.
About the Role
Landmark is seeking a Principal Software Engineer, AI & Developer Experience to define and build the tools, architectures, and engineering practices that enable software engineers and AI development agents to work together effectively. The role spans the complete software development experience, from local development environments and source repositories through build, test, code review, continuous integration and delivery, and the engineering platforms connecting these activities.
This is a hands-on technical leadership role at the intersection of software architecture, Developer Experience, and AI-enabled software development. The successful candidate will lead large-scale engineering initiatives, create reusable tools and reference implementations, improve complex repositories and development systems, and establish the technical foundations for AI-enabled software development across Landmark.
Key Responsibilities
- Define the architecture for AI-enabled software development across Landmark, including how software engineers, AI agents, developer tools, source control, CI/CD systems, and internal engineering platforms work together.
- Establish technical standards for a high-performing developer environment, including local development, build and test workflows, deterministic environments, repository conventions, documentation, and machine-readable interfaces for AI development agents.
- Design and build an AI tooling layer that connects engineers and AI agents with source control, collaboration tools, issue tracking, CI/CD, code review, feature management, security, observability, and internal developer platforms.
- Lead hands-on technical pilots and reference implementations in production software repositories to validate architectural approaches, address complex engineering challenges, and establish repeatable patterns that other development teams can adopt.
- Create automated repository assessments, engineering health indicators, productivity signals, and technical scorecards that identify risks, inconsistencies, and opportunities to improve the developer experience.
- Design, develop, test, troubleshoot, secure, document, and maintain internal developer tools, platform integrations, and engineering automation.
- Develop automated testing and regression strategies that improve software quality, release confidence, and the reliability of AI-assisted development workflows.
- Apply secure software development practices to engineering tools, repositories, build pipelines, and AI-enabled workflows, including the identification and remediation of software vulnerabilities.
- Drive adoption of new engineering tools and technical standards by partnering with software-development teams, gathering feedback, resolving adoption barriers, and continuously improving the developer experience.
- Collaborate with Software Engineering, Architecture, DevOps, Platform Engineering, Security, and other technical stakeholders to define technical direction and deliver scalable engineering solutions.
- Contribute to the technical direction of the growing Developer Experience organization through architectural decisions, technology evaluation, technical standards, reusable solutions, and the development of technical capability across the team.
Qualifications
Required
- An undergraduate degree in Computer Science, Software Engineering, Computer Engineering, or a similar technical discipline.
- A minimum of seven years of related experience, including professional software-development experience.
- Demonstrated experience designing, developing, testing, troubleshooting, securing, documenting, and maintaining software solutions.
- Experience with software engineering workflows such as source control, build automation, automated testing, code review, continuous integration, continuous delivery, or release automation.
- Experience developing or improving software architecture, engineering tools, reusable technical solutions, or development practices in a complex software environment.
- Ability to operate under broad direction, lead technically complex initiatives, collaborate across engineering functions, and influence technical decisions without relying on direct organizational authority.
Preferred
- Experience in Developer Experience, developer productivity, engineering enablement, platform engineering, or internal developer platforms.
- Hands-on experience with AI-assisted software-development tools, coding assistants, AI development agents, agentic workflows, or integrations between AI systems and engineering platforms.
- Experience designing reproducible or deterministic development environments, build systems, repository standards, or repository-governance solutions.
- Experience integrating developer tools with source-control, CI/CD, issue-management, security, feature-management, collaboration, or observability platforms.
- Experience creating reference architectures, technical standards, engineering scorecards, automated repository assessments, or reusable implementation patterns across multiple software-development teams.
Candidates who possess qualifications exceeding the minimum job requirements may be considered for higher-level positions based on experience, additional qualifications, demonstrated capabilities, and current business needs. Depending on education, experience, and skill level, candidates may be eligible for roles ranging from Software Developer Principal to Software Developer Advisor or Software Developer Senior Advisor.
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
Developer Experience | AI-Assisted Software Development | AI Agents | Software Architecture | Internal Developer Platforms | Platform Engineering | Developer Tooling | Engineering Enablement | Source Control | CI/CD | Build Automation | Automated Testing | Repository Governance | Secure Software Development | DevOps | Observability | Technical Standards | Reference Architecture
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 Pkwy E, Houston, Texas, 77032, United States
Job Details
Requisition Number: 212067
Experience Level: Experienced Hire
Job Family: Engineering/Science/Technology
Product Service Line: Landmark Software & Services
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 Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Halliburton, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills in Demand for This Role
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,601 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Halliburton AI Hiring
Halliburton has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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