ZipRecruiter

Log In

1

Machine Learning Petroleum Engineer Jobs in Springfield, MO

MFA Oil Company

Management Trainee

Springfield, MO · On-site

MFA Petroleum Company is an Equal Opportunity/Drug-Free Employer. Responsibilities * Work directly ... Ability to consistently operate a computer and other office productivity machinery * Strong verbal ...

Infra Pipe Solutions Ltd.

Packaging Operator

Springfield, MO · On-site

$15.50 - $18.75/hr

... and moving machinery. * Required to wear appropriate PPE including safety glasses, hearing ... Opportunities for learning and growth in manufacturing. * Competitive compensation and benefits ...

Infrapipes

Packaging Operator

Springfield, MO · On-site

$15.50 - $18.75/hr

... and moving machinery. * Required to wear appropriate PPE including safety glasses, hearing ... Opportunities for learning and growth in manufacturing. * Competitive compensation and benefits ...

Infrapipes

Packaging Operator

Springfield, MO

$15.50 - $18.75/hr

... and moving machinery. * Required to wear appropriate PPE including safety glasses, hearing ... Opportunities for learning and growth in manufacturing. * Competitive compensation and benefits ...

next page

Showing results 1-20

All JobsMachine Learning Petroleum Engineer JobsMachine Learning Petroleum Engineer Jobs in Springfield, MO

Machine Learning Petroleum Engineer information

See Springfield, MO salary details

$28.7K

$117.1K

$176K

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

As of Aug 4, 2026, the average yearly pay for machine learning petroleum engineer in Springfield, MO is $117,132.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,300.00 and $141,000.00 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.
What are popular job titles related to Machine Learning Petroleum Engineer jobs in Springfield, MO? For Machine Learning Petroleum Engineer jobs in Springfield, MO, the most frequently searched job titles are:
What cities near Springfield, MO are hiring for Machine Learning Petroleum Engineer jobs? Cities near Springfield, MO with the most Machine Learning Petroleum Engineer job openings:
Machine Learning Petroleum Engineer jobs near you

Machine Learning Engineer

Bespoke Labs

Springfield, MO

Full-time

Re-posted 18 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

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 preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

Apply