1

Internship Machine Learning Engineer Jobs in Oklahoma

AI Engineer

Oklahoma City, OK ยท On-site

$170K - $200K/yr

... machine learning solutions. The team is composed of professionals across software engineering ... quality assurance, data engineering, and AI, working together to create, maintain, and scale ...

New

Active Professional Google Cloud certifications (specifically Professional Data Engineer, Professional Machine Learning Engineer, or Professional Cloud Architect). * Advanced Agent Deployments:

Posted today

CTIO AI Engineering Manager

Tulsa, OK ยท On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

CTIO AI Engineering Manager

Oklahoma City, OK ยท On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

NGA AI Engineer Manager

Oklahoma City, OK ยท On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

... academic projects or internships). * Experience with embedded systems or real-time Linux ... Any experience or coursework involving Machine Learning / LLMs / AI (especially locally-hosted ...

next page

Showing results 1-20

Internship Machine Learning Engineer information

What does an Internship Machine Learning Engineer do?

An Internship Machine Learning Engineer works alongside experienced engineers to help develop, test, and deploy machine learning models. Their responsibilities may include cleaning and preparing data, writing code for model training, evaluating model performance, and contributing to research tasks. Interns often learn to use popular frameworks such as TensorFlow or PyTorch and gain hands-on experience with real-world datasets. This role is designed to help students or recent graduates apply their academic knowledge to practical problems while developing industry-relevant skills.

What is the difference between Internship Machine Learning Engineer vs Data Scientist Intern?

AspectInternship Machine Learning EngineerData Scientist Intern
Required CredentialsBasic programming, introductory ML knowledgeStatistics, data analysis, programming
Work EnvironmentDeveloping ML models, coding, testingData analysis, visualization, reporting
Employer & Industry UsageTech companies, startups, AI firmsTech, finance, healthcare, consulting

Internship Machine Learning Engineers focus on developing and testing machine learning models, often requiring programming and basic ML knowledge. Data Scientist Interns analyze data, create visualizations, and generate insights. Both roles are common in tech and data-driven industries, but ML Engineer internships emphasize model deployment, while Data Science internships focus on data analysis and reporting.

What types of projects and responsibilities can I expect as an Internship Machine Learning Engineer?

As an Internship Machine Learning Engineer, you will typically support the development, testing, and deployment of machine learning models under the guidance of senior engineers. Your responsibilities may include data preprocessing, exploratory data analysis, implementing algorithms, and evaluating model performance. You'll often collaborate closely with data scientists, software engineers, and product managers, gaining exposure to real-world workflows and tools. This hands-on experience is invaluable for building technical skills and understanding how machine learning solutions are integrated into larger products.

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

To excel as an Internship Machine Learning Engineer, you typically need a solid background in mathematics, programming (especially Python), and foundational machine learning concepts, often supported by coursework or relevant project experience. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is common, along with proficiency in data processing libraries. Curiosity, strong problem-solving abilities, and effective teamwork and communication skills help set candidates apart. These competencies ensure you can contribute meaningfully to projects, adapt to new challenges, and collaborate productively in a rapidly evolving technical environment.
What are the most commonly searched types of Machine Learning Engineer jobs in Oklahoma? The most popular types of Machine Learning Engineer jobs in Oklahoma are:
What cities in Oklahoma are hiring for Internship Machine Learning Engineer jobs? Cities in Oklahoma with the most Internship Machine Learning Engineer job openings:
Senior Engineer, Data Science

Senior Engineer, Data Science

Continental Resources

Oklahoma City, OK โ€ข On-site

$98K - $133K/yr

Full-time

Re-posted 27 days ago


Job description

Job Summary:
Continental Resources is a leading company in the energy sector, and they are seeking a Senior Engineer, Data Science to design and operationalize advanced analytics and AI/ML solutions. This role involves collaborating with various stakeholders to develop data-driven solutions that enhance operations across subsurface, drilling, and production functions.
Responsibilities:
โ€ข Leads the design, development, and deployment of Artificial Intelligence/Machine Learning solutions for upstream subsurface and well operations, including physics-informed and hybrid modeling approaches for reservoir, drilling, and production optimization.
โ€ข Builds advanced Artificial Intelligence/Machine Learning solutions for commercial analytics use cases such as pricing, supply chain, marketing, and trading to improve profitability and decision speed.
โ€ข Executes complex AI initiatives from ideation and discovery through model development, deployment, and sustainment as part of integrated, enterprise-level teams.
โ€ข Architects and implements reliable data pipelines and features using modern data platforms (e.g., Databricks, cloud services), ensuring data quality, lineage, and performance for analytics workloads.
โ€ข Applies Machine Learning Ops best practices to automate training, testing, deployment, monitoring, and model lifecycle management at scale in production environments.
โ€ข Translates complex business problems into analytical approaches with clear hypotheses, success criteria, and measurable outcomes across upstream and commercial domains.
โ€ข Develops and delivers communications that convey a clear understanding of technical concepts, model results, and business implications to diverse technical and non-technical audiences.
โ€ข Builds strong partnerships and cross-functional relationships with geoscience, engineering, operations, commercial, IT, and leadership stakeholders to drive adoption and sustain business impact.
โ€ข Gains the confidence and trust of others through honesty, integrity, and follow-through while championing responsible and secure use of data and AI.
โ€ข Actively seeks new ways to grow and be challenged by staying current on emerging Artificial Intelligence/Machine Learning, generative AI, optimization, and computational techniques relevant to energy and integrating them where they add value.
โ€ข Other duties as assigned.
Qualifications:
Required:
โ€ข Bachelor of Science in Petroleum, Mechanical, Chemical, or related Engineering discipline from an accredited college or university and Master of Science in Data Science, or a closely related data science or analytics field, from an accredited college or university.
โ€ข Minimum five (5) years of hands-on experience delivering production-grade data science/Machine Learning solutions, including end-to-end lifecycle from discovery to deployment and sustainment.
โ€ข Proficiency in Python and SQL; experience with Machine Learning frameworks and tooling (e.g., scikit-learn, PyTorch/TensorFlow), and data platforms such as Databricks and cloud services.
โ€ข Experience building and maintaining data pipelines and features and applying Machine Learning Ops practices for model deployment and monitoring in enterprise environments.
โ€ข Demonstrated ability to partner with technical and business domains in energy, including upstream subsurface, drilling/completions, production operations, and/or commercial analytics such as pricing, supply chain, marketing, or trading.
โ€ข An acceptable pre-employment background and drug test.
Preferred:
โ€ข Oil and gas industry experience, particularly in upstream engineering, subsurface, drilling and completions, production operations, or commercial energy analytics.
โ€ข Background in computational sciences, optimization, or high-performance computing for engineering applications.
โ€ข Familiarity with enterprise data governance, security, and responsible AI practices in regulated environments.
โ€ข Five (5) or more years of combined oil and gas engineering/domain experience and applied data science experience.
Company:
Continental Resources is focused on the exploration and production of onshore oil prone plays and is a Top 10 independent oil producer. Founded in 1967, the company is headquartered in Oklahoma City, USA, with a team of 1001-5000 employees. The company is currently Late Stage.