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Machine Learning Petroleum Engineer Jobs (NOW HIRING)

Qualcomm

Machine Learning Engineer

San Diego, CA · On-site

As a Machine Learning Engineer, you will develop and implement machine learning techniques and collaborate with cross-functional teams to enhance mobile, edge, auto, and IoT products.

Samprasoft

Machine Learning Engineer

Mclean, VA · On-site

GCP/AWS Machine Learning Engineer Freddie Mac iLab is currently looking for Machine Learning Engineers in its Innovation Labs - Tech Strategy team. In this position, you will be responsible for ...

Pelica Health

Machine Learning Engineer

The Machine Learning Engineer will build and manage production machine learning systems, design data pipelines, and collaborate with engineers and product leaders to enhance decision-making processes ...

1872 Consulting

Machine Learning Engineer

Reston, VA · On-site

Machine Learning Engineer D.C. Area About the Position As a member of our Engineering team, you will work with a tight, highly skilled machine learning / data science team dedicated to the ...

Spotify

Machine Learning Engineer

Spotify is a leading music streaming platform, and they are seeking a Machine Learning Engineer to join their Music Promotion team. The role involves building systems to understand the performance of ...

Eccalon LLC

Machine Learning Engineer

Detroit, MI

Machine Learning Engineer Location: Detroit, MI- Onsite Type: Full-time Security Clearance: No clearance required, must be clearable. The Machine Learning Engineer will be an essential member of the ...

Qualcomm

Machine Learning Engineer

San Diego, CA · On-site

$122.80K - $184.20K/yr

Engineering Group, Engineering Group > Machine Learning Engineering General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation ...

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All JobsMachine Learning Petroleum Engineer Jobs

Machine Learning Petroleum Engineer information

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$31.5K

$128.8K

$193.5K

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

As of Jun 4, 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.

What are the key skills and qualifications needed to thrive as a Machine Learning Petroleum Engineer, and why are they important?

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.

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 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 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.

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 May 2026, with employment types broken down into 7% Internship, 89% Full Time, and 4% Temporary. Highlights an 93% In-person, and 7% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.
Machine Learning Engineer

Machine Learning Engineer

Dark Wolf Solutions

Chantilly, VA

Full-time

Posted 21 days ago

Job description

Dark Wolf constructs and deploys data management and analytics solutions for the defense and intelligence communities. We're proud to boast a world-class engineering team that thrives on rolling up their sleeves to solve your mission's biggest challenges.

Dark Wolf is seeking a highly motivated and self-directed professional to fill the role of Machine Learning (ML) Engineer to support our team in Northern Virginia.

Responsibilities:

  • Design, develop, and implement machine learning models and algorithms to solve specific business problems.
  • Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment.
  • Transform machine learning models into deployable APIs and integrate them with existing applications and infrastructure.
  • Collaborate closely with data scientists, software engineers, and product managers to understand requirements and translate them into practical ML solutions.
  • Experiment with different machine learning techniques and algorithms to identify the most effective approaches for given problems.
  • Evaluate model performance using appropriate metrics and iterate on models to improve accuracy, efficiency, and scalability.
  • Monitor and maintain deployed models, ensuring their reliability and performance in production environments.
  • Troubleshoot and resolve issues related to machine learning models and pipelines.
  • Stay up-to-date with the latest advancements in machine learning, deep learning, and related fields.
  • Contribute to the development of best practices and standards for machine learning development and deployment within the team.
  • Document machine learning models, experiments, and deployment processes.
  • Potentially work with large datasets and big data technologies.
  • Optimize machine learning models for performance and efficiency.

Qualifications:

  • Master's in computer science, Machine Learning, or higher level degree is preferred with of 3+ years of related industry experience in Machine Learning, Computer Science, Data Science or related fields.
  • Demonstrated hands-on experience in developing and deploying machine learning models in a production environment.
  • Strong programming skills in Python and experience with relevant machine learning libraries and frameworks such as TensorFlow, Keras, PyTorch, scikit-learn, etc.
  • Solid understanding of machine learning algorithms (e.g., regression, classification, clusting, dimensionality reduction, deep learning architectures).
  • Experience with data preprocessing, feature engineering, and data visualization techniques.
  • Familiarity with data storage and processing technologies (e.g., SQL, NoSQL databases, Spark, Hadoop).
  • Experience with cloud platforms (e.g., AWS, Azure, GCP) and their machine learning services.
  • Understanding of software development principles, version control (e.g., Git), and CI/CD pipelines.
  • Strong analytical and problem-solving skills with the ability to interpret data and draw meaningful conclusions.
  • Excellent communication and collaboration skills to effectively communicate technical concepts to both technical and non-technical audiences.

Preferred Skills:

  • Experience with specific areas of machine learning such as Natural Language Processing (NLP), Computer Vision, or Recommender Systems.
  • Experience with MLOps practices and tools for automating and monitoring machine learning workflows.
  • Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes.
  • Experience with building and deploying RESTful APIs.
  • Familiarity with big data technologies and distributed computing.
  • Experience with statistical modeling and inference.

Position Clearance Requirement:

TS/SCI with Full-Scope Polygraph

This position is located in Chantilly/Herndon, VA.

We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.