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Scientific Machine Learning Jobs in Ohio (NOW HIRING)

$28 - $45/hr

The intern will work closely with Data Scientists and Software Engineers to develop, train, evaluate, and deploy machine learning models that solve real-world business problems. Key Responsibilities ...

$28 - $45/hr

The intern will work closely with Data Scientists and Software Engineers to develop, train, evaluate, and deploy machine learning models that solve real-world business problems. Key Responsibilities ...

$28 - $45/hr

The intern will work closely with Data Scientists and Software Engineers to develop, train, evaluate, and deploy machine learning models that solve real-world business problems. Key Responsibilities ...

... 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 ...

... 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 ...

... 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 ...

... 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 ...

Requires a Bachelor's degree in Computer Science, Mathematics, or Statistics, and a minimum of 8 ... Prior success in deploying impactful Machine Learning solutions to large-scale production systems ...

Machine Learning Engineer II

Columbus, OH · On-site

$94K - $128K/yr

In this role, you will embrace the role of "full-stack" data scientist, which will often require ... Machine Learning engineers at Mimecast are empowered to use AI development tools every day-to ...

Machine Learning Engineer II

Columbus, OH · On-site

$94K - $128K/yr

In this role, you will embrace the role of "full-stack" data scientist, which will often require ... Machine Learning engineers at Mimecast are empowered to use AI development tools every day-to ...

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Scientific Machine Learning information

Is ML a high paying job?

Scientific Machine Learning roles typically offer high salaries due to the specialized skills required, such as expertise in deep learning, data analysis, and programming with tools like Python and TensorFlow. Compensation varies by industry, experience, and location but generally exceeds average tech salaries for comparable roles.

Which 3 jobs will survive AI?

Scientific Machine Learning professionals will likely continue to be in demand due to their expertise in developing and applying AI models to complex scientific problems. Roles such as data scientists, AI researchers, and machine learning engineers are expected to persist because they require specialized knowledge, critical thinking, and ongoing innovation that AI tools complement rather than replace. These jobs often involve interdisciplinary skills, programming, and understanding of domain-specific data, making them more resilient to automation.

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 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.

How much does a machine learning scientist make?

A machine learning scientist typically earns between $90,000 and $150,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in deep learning or natural language processing can earn higher salaries, often exceeding $180,000.

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 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.

Is 40 too late for data science?

Scientific Machine Learning roles often value skills and experience over age, and many professionals transition into data science or machine learning at various stages of their careers. Learning relevant tools like Python, TensorFlow, or scikit-learn and gaining practical experience can help regardless of age, making 40 not too late to pursue this field.
What are popular job titles related to Scientific Machine Learning jobs in Ohio? For Scientific Machine Learning jobs in Ohio, the most frequently searched job titles are:
What cities in Ohio are hiring for Scientific Machine Learning jobs? Cities in Ohio with the most Scientific Machine Learning job openings:
Infographic showing various Scientific Machine Learning job openings in Ohio as of June 2026, with employment types broken down into 78% Full Time, and 22% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution.

$28 - $45/hr

Other

Posted yesterday


Job description

Machine Learning Engineer Intern

United States
Internship | Full-Time (40 hours/week)
Pay Range: $28 – $45 per hour 
Visa: H1B Sponsorship Available | STEM OPT, OPT & CPT Candidates Welcome


Position Overview

We are seeking a highly motivated Machine Learning Engineer Intern to join our AI/ML team. This role is ideal for students or entry level candidates in STEM fields who are passionate about building scalable machine learning models and deploying them into production environments.

The intern will work closely with Data Scientists and Software Engineers to develop, train, evaluate, and deploy machine learning models that solve real-world business problems.


Key Responsibilities
  • Assist in building and training machine learning and deep learning models

  • Perform data preprocessing, feature engineering, and exploratory data analysis (EDA)

  • Implement supervised and unsupervised learning algorithms

  • Optimize model performance using hyperparameter tuning

  • Deploy ML models using REST APIs or cloud services

  • Work on model monitoring, validation, and performance tracking

  • Collaborate with cross-functional teams in Agile/Scrum environment

  • Document experiments and maintain reproducible ML workflows


Required Qualifications
  • Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or related STEM field

  • Strong understanding of Machine Learning fundamentals

  • Knowledge of Probability, Statistics, and Linear Algebra

  • Basic understanding of Data Structures and Algorithms


Technical Skills (ATS Keywords)Programming Languages
  • Python

  • R (preferred)

  • Java (basic knowledge)

  • SQL

Machine Learning & AI Frameworks
  • Scikit-learn

  • TensorFlow

  • Keras

  • PyTorch

  • XGBoost

  • LightGBM

Data Processing & Big Data
  • Pandas

  • NumPy

  • Apache Spark

  • PySpark

  • Hadoop

NLP & Advanced Techniques (Preferred)
  • Natural Language Processing (NLP)

  • Computer Vision

  • Deep Learning

  • Transformers

  • LLM fundamentals

Cloud & MLOps
  • AWS (SageMaker, S3, EC2)

  • Microsoft Azure ML

  • Google Cloud AI Platform

  • Docker

  • Kubernetes

  • MLflow

  • CI/CD pipelines

  • Model Deployment & Monitoring

Tools & Concepts
  • Git

  • REST APIs

  • Feature Engineering

  • Model Evaluation Metrics

  • A/B Testing

  • Agile/Scrum


Preferred Qualifications
  • Prior ML internship or academic research experience

  • Experience deploying models into production

  • Knowledge of MLOps practices

  • Strong problem-solving and analytical skills

  • Good communication and teamwork abilities


Compensation & Benefits
  • Competitive hourly compensation ($28 – $45/hr)

  • Hands-on real-world AI/ML project experience

  • Mentorship from senior ML engineers

  • Opportunity for full-time conversion

  • H1B sponsorship support for eligible candidates

  • STEM OPT extension support


Equal Opportunity Employer

We are an Equal Opportunity Employer and encourage applications from diverse backgrounds, including international students and professionals requiring H1B sponsorship or STEM OPT support.