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Machine Learning Petroleum Engineer Jobs (NOW HIRING)

Jobgether

As a Machine Learning Engineer, you will work with complex datasets, design and optimize models, and help bring intelligent solutions into production. You will collaborate with software engineers and ...

Forhyre

Machine Learning Engineer

New York, NY

We are looking for a Machine Learning Engineer to help us create artificial intelligence products. Machine Learning Engineer responsibilities include creating machine learning models and retraining ...

2T Consulting

Machine Learning Engineer

Seattle, WA · On-site

Develop platform-level tools for prompt engineering, automated evaluation, prompt optimization, and experimentation. * Deploy, monitor, and maintain machine learning and generative AI models in ...

New

6AM City, LLC

Machine Learning Engineer

Manhattan, NY · On-site

$90 - $130/hr

Machine Learning Engineer responsibilities include creating machine learning models and retraining systems. To do this job successfully, you need exceptional skills in statistics and programming. If ...

New

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

Rimkus

Forensic Petroleum Engineer

Dallas, TX · On-site

Join Rimkus and unlock your potential with endless opportunities for growth, learning, and making a ... Petroleum/Mechanical Engineering degree or higher. * P.E. required and the ability to obtain ...

LHi Group Ltd

Machine Learning Engineer

Calumet, PA · On-site

Machine Learning Engineer Remote (US, Canada & Europe) We're partnering with one of the world's fastest-growing gaming technology companies, building machine learning systems that power some of the ...

New

Advatix Inc.

Machine Learning Engineer

San Mateo, CA · On-site

$110 - $165/hr

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift: N/A Location: San Mateo, CA (Peninsula) - Onsite Preferred Schedule: Full time, Permanent Role Visa ...

New

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Showing results 21-40

All JobsMachine Learning Petroleum Engineer Jobs

Machine Learning Petroleum Engineer information

See salary details

$31.5K

$128.8K

$193.5K

How much do machine learning petroleum engineer jobs pay per year?

As of Aug 7, 2026, the average yearly pay for machine learning petroleum engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

How does a machine learning petroleum engineer typically collaborate with geoscientists and drilling teams to optimize oil and gas production?

A Machine Learning Petroleum Engineer works closely with geoscientists and drilling teams by integrating data-driven models into exploration and production workflows. They analyze geological, seismic, and operational data to develop predictive algorithms that identify optimal drilling locations, forecast reservoir performance, and improve recovery rates. Regular collaboration involves translating complex data insights into actionable recommendations that guide drilling strategies and inform real-time decisions, ensuring all teams are aligned to maximize efficiency and safety. This multidisciplinary approach fosters continuous learning and innovation across teams.

What is the difference between Machine Learning Petroleum Engineer vs Reservoir Engineer?

AspectMachine Learning Petroleum EngineerReservoir Engineer
Required CredentialsBachelor's/Master's in Petroleum Engineering, Data Science, or related fields; knowledge of machine learningBachelor's/Master's in Petroleum Engineering or Geosciences; strong understanding of reservoir simulation
Work EnvironmentData analysis, modeling, software development in oil & gas companiesReservoir modeling, field development planning in oil & gas operations
Industry UsageApplying machine learning to optimize extraction, predict reservoir behaviorEstimating reservoir properties, managing production strategies

The Machine Learning Petroleum Engineer focuses on integrating data science and machine learning techniques to optimize oil extraction processes, while the Reservoir Engineer specializes in modeling and managing subsurface reservoirs to maximize recovery. Both roles are vital in the oil & gas industry but differ in their core skills and daily tasks.

What is a machine learning petroleum engineer?

A Machine Learning Petroleum Engineer is a specialist who combines expertise in petroleum engineering with machine learning and data science techniques. They use advanced algorithms and data analytics to optimize oil and gas exploration, drilling, production, and reservoir management. Their work helps improve decision-making, reduce operational costs, and increase efficiency by analyzing large datasets from various sources such as sensors, seismic data, and production logs. These professionals often work closely with geoscientists, data engineers, and other stakeholders in the energy sector.

What are the key skills and qualifications needed to thrive as a machine learning petroleum engineer?

To thrive as a Machine Learning Petroleum Engineer, you need a strong background in petroleum engineering, programming (such as Python or R), and applied machine learning, usually supported by a relevant engineering degree. Familiarity with data analysis platforms, machine learning frameworks (like TensorFlow or Scikit-learn), and petroleum industry software (such as Petrel or Eclipse) is essential. Strong analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for integrating technical insights with business goals. These competencies enable the effective application of data-driven solutions to optimize exploration, production, and operational efficiency in the energy sector.
More about Machine Learning Petroleum Engineer jobs
What cities are hiring for Machine Learning Petroleum Engineer jobs? Cities with the most Machine Learning Petroleum Engineer job openings:
What states have the most Machine Learning Petroleum Engineer jobs? States with the most job openings for Machine Learning Petroleum Engineer jobs include:
What job categories do people searching Machine Learning Petroleum Engineer jobs look for? The top searched job categories for Machine Learning Petroleum Engineer jobs are:
Machine Learning Petroleum Engineer jobs near you
Infographic showing various Machine Learning Petroleum Engineer job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer

Jobgether

On-site, Remote

Full-time

Posted 9 days ago

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer based in Netherlands.

This role offers the opportunity to build and improve AI-powered products by developing machine learning solutions that address real-world challenges.
As a Machine Learning Engineer, you will work with complex datasets, design and optimize models, and help bring intelligent solutions into production.
You will collaborate with software engineers and technical teams in a fast-moving, remote environment focused on innovation.
The position combines data analysis, experimentation, software development, and machine learning engineering to create impactful products.
You will have the opportunity to work on challenging technical problems while continuously expanding your expertise in emerging AI technologies.
This role is ideal for a curious and motivated engineer who enjoys building practical AI applications and solving meaningful problems.

Accountabilities:

The Machine Learning Engineer will contribute to the development, deployment, and improvement of AI-driven solutions. The role requires strong technical skills, analytical thinking, and a passion for building scalable machine learning systems. Key responsibilities include:

  • Develop, train, test, and evaluate machine learning models to support product objectives.
  • Prepare, clean, analyze, and manage datasets used for model training and validation.
  • Improve model performance through experimentation, optimization, and continuous testing.
  • Deploy, maintain, and monitor machine learning models in production environments.
  • Collaborate with software engineers to integrate machine learning capabilities into products and applications.
  • Track model performance and identify opportunities to improve accuracy, reliability, and efficiency.
  • Apply modern machine learning techniques and stay informed about emerging technologies and best practices.
  • Write clean, maintainable, and efficient code to support scalable AI solutions.
  • Contribute to technical discussions and help solve complex engineering challenges.
  • Participate in the continuous improvement of machine learning workflows and development processes.
Requirements:

The ideal candidate is a technically curious and motivated professional with a foundation in machine learning, software engineering, or data science. Required qualifications and skills include:

  • 1+ year of experience in machine learning, software engineering, data science, or a related technical field, or strong personal projects demonstrating machine learning capabilities.
  • Basic understanding of machine learning concepts, algorithms, and model development processes.
  • Strong programming skills in Python.
  • Familiarity with machine learning frameworks and libraries such as PyTorch, TensorFlow, or scikit-learn.
  • Comfortable working with data and writing clean, maintainable code.
  • Experience using Git and version control practices.
  • Strong problem-solving skills with the ability to analyze technical challenges.
  • Good written and verbal English communication skills.
  • Passion for learning, experimenting, and building AI-powered solutions.
  • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field is a plus but not mandatory.
  • Experience with large language models (LLMs) is an advantage.
  • Familiarity with cloud platforms such as AWS, GCP, or Azure is beneficial.
  • Knowledge of SQL and experience deploying machine learning models are considered advantages.
  • Personal AI, machine learning projects, or open-source contributions are highly valued.
Benefits:

The role offers a flexible remote environment and the opportunity to contribute to innovative AI solutions while growing professionally. Benefits include:

  • Fully remote work opportunity.
  • Flexible working environment with autonomy and work-life balance.
  • Opportunity to build and improve real-world AI products.
  • Exposure to challenging technical problems and modern machine learning technologies.
  • Collaborative and fast-moving team environment.
  • Opportunities for professional growth and continuous learning.
  • Competitive compensation based on experience.
  • Opportunity to contribute to impactful machine learning projects from anywhere.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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