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Machine Learning Petroleum Engineer Jobs in Missouri

Jobtailor

Machine Learning Engineer

California, MO · On-site

$130 - $190/hr

PhD in STEM +0 years of relevant experience or equivalent related work experience * 5+ years of experience in data engineering, machine learning engineering, or related roles * Data Pipeline ...

iHerb

Principal Machine Learning Engineer

California, MO · On-site

$205 - $230/hr

Job Summary The Machine Learning Engineer will tackle challenging problems and create scalable machine learning systems and platforms that make an impact on millions of users. This role will work ...

6AM City, LLC

Machine Learning Engineer

California, MO · On-site

$110 - $170/hr

As a Machine Learning Integration Engineer, you will help rapidly prototype, mature, and monitor ML/CV solution that are integral to Turion's Space Domain Awareness data products. You will work on ...

Jobgether

$94K - $124K/yr

We are seeking a Senior Geospatial Machine Learning Engineer to develop advanced AI solutions that transform satellite and environmental data into actionable insights. This role sits at the ...

Bespoke Labs

Machine Learning Engineer

Columbia, MO · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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Machine Learning Petroleum Engineer information

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.

What are popular job titles related to Machine Learning Petroleum Engineer jobs in Missouri?

For Machine Learning Petroleum Engineer jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Machine Learning Petroleum Engineer jobs in Missouri look for?

The top searched job categories for Machine Learning Petroleum Engineer jobs in Missouri are:

What cities in Missouri are hiring for Machine Learning Petroleum Engineer jobs?

Cities in Missouri with the most Machine Learning Petroleum Engineer job openings:

Machine Learning Petroleum Engineer jobs near you
Infographic showing various Machine Learning Petroleum Engineer job openings in Missouri as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Machine Learning Engineer

Jobtailor

California, MO • On-site

$130 - $190/hr

Other

Posted 8 days ago

Job description

Responsibilities
  • build dynamic troubleshooting agents that understand networks
  • solve unstructured production log data complexities
  • optimize hardware utilization for data collection
  • automate synthetic datasets creation
  • architect data infrastructure for real-time network failures analysis
  • design and scale automated pipelines transforming raw production logs into insights
  • develop systems generating synthetic data for edge cases learning
  • tackle unique network complexity problems
  • optimize data collection and hardware utilization
Requirements
  • Bachelor's degree in STEM and 5+ years of relevant experience
  • Master's degree in STEM and 3+ years of relevant experience
  • PhD in STEM +0 years of relevant experience or equivalent related work experience
  • 5+ years of experience in data engineering, machine learning engineering, or related roles
  • Data Pipeline experience, designing and scaling data pipelines for unstructured or semi-structured data, including ingestion, cleansing, and auditing
  • ML Infrastructure experience working with ML data workflows, including dataset creation, labeling, and evaluation
  • Experience with Python and data processing frameworks (e.g., Spark, Beam, Ray)
  • Experience with ML systems and tools, such as training pipelines and model evaluation frameworks
  • Experience with human-in-the-loop ML systems, active learning, weak supervision or self-evolving agents (preferred)
  • Exposure large language models, computer vision, or speech datasets (preferred)
  • Experience building internal tools or platforms used by annotation or operations teams (preferred)
Hard Skills
  • Data Engineering
  • Machine Learning Engineering
  • Data Pipeline
  • Data Processing
  • Synthetic Data Creation
  • Real-Time Analysis
  • Network Troubleshooting
  • Data Cleansing
  • Model Evaluation
  • Active Learning
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