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Scientific Machine Learning Jobs in Oklahoma (NOW HIRING)

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

New

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

New

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

New

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

New

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

New

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

New

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Machine Learning Tutor

Tulsa, OK · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Oklahoma?

For Scientific Machine Learning jobs in Oklahoma, the most frequently searched job titles are:

What cities in Oklahoma are hiring for Scientific Machine Learning jobs?

Cities in Oklahoma with the most Scientific Machine Learning job openings:

Infographic showing various Scientific Machine Learning job openings in Oklahoma as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Summer 2027 Intern - Machine Learning Engineering

Workiva

On-site, Remote

$40/hr

Full-time, Temporary, Internship

Retirement

Posted 2 days ago

New


Workiva rating

9.9

Company rating: 9.9 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

1st of 247 rated software companies


Job description

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the development, deployment, and monitoring of machine learning models. Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management and Analytics organization. This is an excellent opportunity to gain hands-on experience in the machine learning lifecycle.

What You'll Do

  • Participate in discovery, requirements gathering, and prototyping of new tools and libraries
  • Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer
  • Help integrate tools such as LlamaIndex and LlamaParse into existing workflows
  • Assist in building and deploying AI Agents to automate data processing and analysis tasks
  • Participate in code reviews
  • Implement and update tests (unit, integration)
  • Track tasks and complete status updates using internal tools
  • Work in an Agile development methodology alongside your teammates

What You'll Need

Minimum Qualifications

  • Currently pursuing a bachelor’s degree or higher in Statistics, Mathematics, Computer Science, Physics, Electrical Engineering, or related field of study
  • Possess solid programming skills
  • Basic experience with source control systems such as Git

Preferred Qualifications

  • Work effectively within a geographically distributed team
  • Demonstrate strong communication and organizational skills
  • Familiarity with the data science lifecycle and basic machine learning concepts
  • Experience with containerization tools such as Docker and Kubernetes
  • Knowledge of cloud platforms such as AWS
  • Proficiency with Python and/or Go
  • Familiarity with REST APIs

Travel Requirements & Working Conditions

  • Minimal travel
  • Reliable internet access for any period of time working remotely, not in a Workiva office

Location: This internship is primarily a remote opportunity. However, if you are located near one of our office hubs, you are welcome to work in a hybrid capacity and utilize our office spaces. Check out our article on Workplace Flexibility to learn more.

When can you expect to hear back?

We are committed to attending all career fairs and recruitment events before closing our positions. That means, this position might be open without updates for a few weeks to give us time to connect with all potential candidates before wrapping up the recruitment season. Check out our tentative timeline below to see when you can expect to hear from us!

Postings close: September 27, 2026

Engineering postings close: October 2, 2026

Interviews: Early to mid October

Offers: Late October

2027 Start Dates:

This position has opportunities to start in the Spring or Summer. Please see our start dates below and let us know your availability in your application.

  • Spring 2027 Internships: Monday, January 4, 2027 (15-20 hours per week max)
  • Summer 2027 Internships: Monday, May 17, 2027 (40/hours per week max)

How You’ll Be Rewarded

Salary range in the US: $40.00 - $40.00 401(k) participation and match

Paid sick leave

A unique opportunity to further your learning experience through additional internship seasons

Why Join Workiva

Workiva is the platform designed to bring confidence, control, and a competitive edge to the world’s most complex organizations. Our AI-powered platform unifies finance, risk, and sustainability on a single, secure foundation-ensuring data is trusted, traceable, and ready to act on. With an unbroken path from source to output, leaders gain confidence in their numbers, visibility into current and emerging risks, and the ability to move with speed and precision in a constantly changing world.

At Workiva, you’ll bring technology to market that executives, boards, and regulators depend on. The work you do here helps organizations navigate uncertainty, maintain trust, and make decisions that stand up to scrutiny. If you’re energized by meaningful challenges, inspired by collaborative teams, and motivated to help organizations turn uncertainty into advantage, we’d love to meet you.

Employment decisions are made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic.

Workiva is committed to working with and providing reasonable accommodations to applicants with disabilities. To request assistance with the application process, please email earlycareer@workiva.com .

Workiva employees are required to undergo comprehensive security and privacy training tailored to their roles, ensuring adherence to company policies and regulatory standards.

Workiva supports employees in working where they work best - either from an office or remotely from any location within their country of employment.


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