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About This Role
Texas, United States of America
Job Family Group:
Geosciences
Worker Type:
Regular
Posting Start Date:
October 2, 2026
Business Unit:
Upstream
Experience Level:
Experienced Professionals
Job Description:
What’s the role
Shell is seeking an AI Embedment Lead to join its team in Houston, TX. The ideal candidate combines deep expertise in petrophysics and production engineering with the ability to bridge industry knowledge and practical AI development and implementation.
This role sits within a highly skilled and dynamic team of domain, AI/ML, and analytics experts focused on identifying the most impactful opportunities and delivering measurable results at pace. The team operates across Shell’s global portfolio and is empowered to challenge conventional approaches, test new ideas, and rapidly configure, customize, and deploy solutions in live asset environments.
Individuals in this team are expected to operate with a high degree of autonomy and creativity, embracing ambiguity and taking calculated risks to unlock value. Success is defined not only by technical excellence but also by the ability to push boundaries, move quickly from concept to execution, and drive tangible business outcomes.
The AI Embedment Lead is responsible for identifying, adapting, implementing, and embedding AI-driven solutions within Shell’s operated upstream oil and gas assets to deliver measurable operational and financial value. This role bridges domain expertise and advanced AI capabilities, ensuring solutions are not only technically sound but also aligned with real business challenges and successfully adopted in the field.
The position focuses on developing bespoke, high-impact solutions, including generative AI tools, agentic AI workflows, and advanced analytics. The successful candidate will work closely with asset teams to configure, customize, deploy, and scale solutions while identifying opportunities for repeatability and value creation across multiple assets.
What you’ll be doing
- Together with together with our customer success team, partner with asset leadership, subsurface, wells, and operations teams to identify business problems where AI can create meaningful value
- Apply strong subsurface & wells domain knowledge to prioritize use cases with clear operational or economic impact
- Translate ambiguous business challenges into actionable digital/AI solution opportunities
- Act as a translator between innovation & products teams and business stakeholders
- Ensure AI solutions reflect the realities of subsurface & wells workflows (e.g., uncertainty, data quality, decision cycles)
- Provide domain-informed guidance on model design, outputs, and usability
- Lead the implementation of: + Generative AI solutions, including custom code and workflows + Agentic AI systems, ranging from task automation to semi-autonomous decision support + Advanced analytics solutions, including data ingestion, pattern recognition, and predictive insights
- Collaborate with data scientists, engineers, and platform teams to build robust solutions
- Work directly within assets to ensure solutions are embedded into day-to-day workflows
- Drive adoption through training, change management, and iterative refinement
- Ensure solutions are operationally reliable, scalable, and user-centric
- Identify components of bespoke solutions that can be reused or scaled across other assets
- Contribute to building reusable frameworks, libraries, and best practices
- Share learnings across the organization to accelerate adoption and value capture
- Ensure solutions adhere to company standards for data governance, security, and responsible AI
Validate outputs and ensure appropriate human oversight in decision-making contexts *
What you bring
- Must have legal authorization to work in the US on a full-time basis for anyone other than current employer
- Bachelor's degree required; MS/PhD in Geoscience or Petroleum Engineering, Reservoir Engineering, or related discipline is preferred
- At least 10 years of relevant experience in the Upstream Oil & Gas industry is preferred
- Deep expertise in petrophysics / formation evaluation or production engineering / production technology; other subsurface and wells disciplines considered Proven experience developing, implementing, or building and deploying Agentic AI and Generative AI (GenAI) solutions
- Strong proficiency in Python and AI/ML solution development
- Hands-on building of LLM applications and agentic systems, including evaluation of their outputs
- Regular use of AI coding assistants and agents
- Experience with data analytics, statistics, and/or visualization tools such as Databricks, Plotly Dash, and Spotfire
- Knowledge of data pipelines, solution architecture, and deployment best practices
- Proven experience delivering production-ready solutions
- Design-thinking mindset with a track record of driving technology innovation and adoption
Excellent communication, collaboration, and relationship-building skills across cross-functional teams and stakeholders *
What we offer
The base salary range for this position is $201,000 - $301,000 per year. Individual pay will be based on various factors, such as relevant work experience, education, certifications, skill level, seniority, and internal equity.
For regular full-time or regular part-time employees of the Company (participating companies as listed in the Summary Plan Description), insurance coverage options include medical, dental, vision coverage, life Insurance, Business Travel Accident Insurance, and Occupational Accidental Death Benefit programs. Employees also participate in a company pension plan and a 401(k) plan. Paid leave includes up to 6 weeks of paid vacation time, up to 11 paid holidays, and parental leave offering 16 weeks of paid leave for birthing parents, and 8 weeks of paid leave for non-birthing parents.
Additionally, employees are eligible for short-term disability leave for up to 26 weeks at 100% or 50% of base pay as well as Long-Term Disability insurance. Shell also offers other compensation such financial reimbursement for adoption, wellness, education, and personal learning expenses, and some roles are eligible for discretionary long-term incentives. For interns, eligible benefits include medical, dental, and vision coverage, life insurance, Business Travel Accident Insurance, and Occupational Accidental Death Benefit programs; participation in a 401(k) plan; and paid leave for up to 11 paid holidays. Additional information on Shell’s US benefit programs can be found at https://www.shell.us/careers/about-careers-at-shell/rewards-and-benefits.html .
You bring your skills and experience to Shell and in return you work with talented, committed people on one of the most important challenges facing our planet. You’ll have the opportunity to develop the skills you need to grow in an environment where we value honesty, integrity, and respect for one another. You’ll be able to balance your priorities as you become the best version of yourself.
- Progress as a person as we work on the energy transition together
- Continuously grow the transferable skills you need to get ahead
- Work at the forefront of technology, trends, and practices
- Collaborate with experienced colleagues with unique expertise
- Achieve your balance in a values-led culture that encourages you to be the best version of yourself
- Benefit from flexible working hours, and the possibility of remote/mobile working
- Perform at your best with a competitive starting salary and annual performance related salary increase – our pay and benefits packages are considered to be among the best in the world.
- Take advantage of paid parental leave, including for non-birthing parents
- Join an organization working to become one of the most diverse and inclusive in the world. We strongly encourage applicants of all genders, ages, ethnicities, cultures, abilities, sexual orientation, and life experiences to apply
- Grow as you progress through diverse career opportunities in national and international teams
Gain access to a wide range of training and development programs *
We'd like you to know that Shell has a bold goal: to become one of the world’s most diverse and inclusive companies. .
Shell in The United States
With more than 11,000 employees, Shell has been in the US for more than 100 years and is delivering secure energy supplies across America.
From producing a reliable supply of oil and gas in the US Gulf of America to biofuels, our retail sites and EV charging network, to marketing and trading electricity generated by gas plants, solar and wind power, we are one of the few countries where every Shell business is represented.
We are working with our customers to deliver the energy the world needs today and building the energy systems of tomorrow, all while creating value for society and shareholders.
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DISCLAIMER:
Please note: We occasionally amend or withdraw Shell jobs and reserve the right to do so at any time, including prior to the advertised closing date. Before applying, you are advised to read our data protection policy. This policy describes the processing that may be associated with your personal data and informs you that your personal data may be transferred to Shell/Shell Group companies around the world. The Shell Group and its approved recruitment consultants will never ask you for a fee to process or consider your application for a career with Shell. Anyone who demands such a fee is not an authorised Shell representative and you are strongly advised to refuse any such demand. Shell participates in E-Verify. All qualified applicants will receive consideration for employment without regard to race, color, sex, national origin, age, religion, disability, sexual orientation, gender identity, protected veteran status, citizenship, genetic information or other protected status under federal, state or local laws. Shell is an Equal Opportunity Employer - Minorities/Females/Veterans/Disability. As a US Federal Contractor, hiring selections are subject to periodic audit review and documentation of your selections should be maintained for a period of three calendar years. It is the policy of Shell in the U.S. (“Shell”) to provide equal opportunity to all individuals, employees and all qualified applicants for employment consistent with employment requirements and qualifications. Shell prohibits discrimination based on race, color, sex, national origin, age, religion, disability, sexual orientation, gender identity, veteran status, citizenship, genetic information, or other protected status under federal, state or local laws. All employees are expected to support this policy and contribute to an environment of equal opportunity. If you need an accommodation for a disability during the resourcing process, please speak with an HR representative.
Salary Context
This $201K-$301K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Shell, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($251K) sits 18% above the category median. Disclosed range: $201K to $301K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Shell AI Hiring
Shell has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Houston, TX, US, Singapore, Singapore. Compensation range: $301K - $301K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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