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Reinforcement Learning Engineer Jobs in Ohio (NOW HIRING)

Goosehead

Data Scientist

Westlake, OH · On-site

Advanced understanding of LLMs and generative AI, including fine-tuning, RAG pipelines, and prompt engineering. * Familiarity with causal inference, uplift modeling, or reinforcement learning in real ...

LG

Civil Engineering Co-op/Internship

Jeffersonville, OH · On-site

$16.50 - $21.50/hr

... engineer: * Analysis of existing trusses for potential reinforcement * Verification of field ... Our Cooperative Learning Internships program is utilized as a talent pipeline for our full-time ...

LG

Civil Engineering Co-op/Internship

Jeffersonville, OH

$16.50 - $21.50/hr

... engineer: * Analysis of existing trusses for potential reinforcement * Verification of field ... Our Cooperative Learning Internships program is utilized as a talent pipeline for our full-time ...

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All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Ohio

Reinforcement Learning Engineer information

See Ohio salary details

$36.1K

$110.2K

$182.1K

How much do reinforcement learning engineer jobs pay per year?

As of Jul 19, 2026, the average yearly pay for reinforcement learning engineer in Ohio is $110,152.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,900.00 and $144,000.00 per year, depending on experience, location, and employer.

What are Reinforcement Learning Engineers?

Reinforcement Learning Engineers are specialized professionals who design, develop, and implement algorithms based on reinforcement learning, a type of machine learning where agents learn to make decisions by receiving rewards or penalties. They work on building models that enable machines to learn optimal actions through trial and error in complex environments. Their responsibilities often include developing RL architectures, tuning hyperparameters, running simulations, and applying RL methods to real-world problems like robotics, gaming, or recommendation systems. RL Engineers typically have strong backgrounds in computer science, mathematics, and deep learning, along with experience in programming languages like Python and frameworks such as TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive as a Reinforcement Learning Engineer, and why are they important?

To thrive as a Reinforcement Learning Engineer, you need a strong background in machine learning, mathematics (especially probability and statistics), and programming languages like Python, often supported by a relevant degree in computer science or engineering. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like OpenAI Gym), and cloud computing platforms is typically required. Problem-solving skills, creativity, and effective collaboration help set outstanding engineers apart in this field. These competencies enable the design and deployment of advanced RL solutions that address real-world challenges and drive innovation.

What are some common challenges faced by Reinforcement Learning Engineers when deploying models in real-world environments?

One of the main challenges Reinforcement Learning (RL) Engineers face is bridging the gap between simulation and real-world deployment. Models that perform well in controlled environments may struggle with unpredictable data, safety constraints, or limited feedback in production. Additionally, RL algorithms often require significant computational resources and careful tuning to avoid instability. Collaboration with domain experts and software engineers is essential to address these issues and ensure successful integration of RL solutions into existing systems.

What is the difference between Reinforcement Learning Engineer vs Machine Learning Engineer?

AspectReinforcement Learning EngineerMachine Learning Engineer
CredentialsBachelor's/Master's in CS, AI, or related; experience with RL frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on RL applicationsTech companies, data-driven firms, AI departments across industries
Industry UsageSpecialized in RL projects like robotics, game AI, autonomous systemsBroader applications including predictive modeling, NLP, computer vision

Reinforcement Learning Engineers focus on developing algorithms that learn through interactions with environments, often in robotics or gaming. Machine Learning Engineers work on a wider range of models and applications. While both roles require strong programming and math skills, RL Engineers specialize in sequential decision-making, whereas ML Engineers handle diverse data-driven tasks across industries.

What are popular job titles related to Reinforcement Learning Engineer jobs in Ohio? For Reinforcement Learning Engineer jobs in Ohio, the most frequently searched job titles are:
What job categories do people searching Reinforcement Learning Engineer jobs in Ohio look for? The top searched job categories for Reinforcement Learning Engineer jobs in Ohio are:
What cities in Ohio are hiring for Reinforcement Learning Engineer jobs? Cities in Ohio with the most Reinforcement Learning Engineer job openings:
Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Ohio as of July 2026, with employment types broken down into 91% Full Time, 7% Part Time, and 2% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $110,152 per year, or $53 per hour.
Payment Optimization Data Scientist II

Payment Optimization Data Scientist II

Worldpay

Cincinnati, OH • On-site, Remote

Full-time

Posted 18 days ago

Job description

Job Description

Are you ready to unleash your full potential? We're looking for people who are passionate about payments to chart Worldpay's path to being the largest and most-loved payments company in the world.

About the team

Worldpay, LLC seeks Payment Optimization Data Scientist II in Cincinnati, OH to employ machine learning and statistical modeling to create and enhance data driven products.

What you will be doing

The Payment Optimization Data Scientist II will analyze and extract insights from internal and external data. Additionally, the role will:

  • Work with big data and transform complex datasets into more usable formats.
  • Work with a variety of data science tools and programming languages such as SAS, PYTHON, R, SCALA, SQL.
  • Work independently and collaborate with other groups to solve complex problems.
  • Create and present analyses to internal and external partners and clients.
  • Document models and write code to track and monitor models and product performance.
  • Understand the realities of model development and make pragmatic and business-aware choices when trading-off sophistication and accuracy versus implementation and performance costs.
  • Perform other related duties assigned as needed.

Requirements

Master's degree or foreign equivalent in Computer Science, Data Science, Computer Engineering, or related field and four (4) years of experience in the job offered or a related occupation:

  • developing and deploying supervised machine learning models including CatBoost and XGBoost boosting/ensemble methods for Dynamic Transaction Retry, Time-of-Day optimization, and Intelligent Payment Orchestration to improve payment acceptance/approval rates and subscription recovery metrics;
  • applying Multi-Armed Bandits Reinforcement Learning and Causal Inference techniques to production systems for continuous optimization and feature/data updating including measuring improvements via AUC scores and acceptance rate uplift;
  • consolidating and normalizing disparate payment gateway response codes/ large-scale financial transaction data into consistent feature sets for ML (Machine Learning) models to improve model interpretability and predictive accuracy;
  • performing segmentation analysis using unsupervised ML algorithms with K-Means and DBSCAN to enhance data quality and inform supervised models for chargeback prediction and risk mitigation;
  • performing large-scale feature extraction and processing from relational and cloud-based systems with MySQL, BigQuery, & Snowflake and integrating these derived features into models running on Databricks/Apache Spark;
  • designing, executing, and analyzing A/B tests and pre-post analyses, and utilizing time-series modeling techniques to monitor and mitigate data drift and model drift in high-frequency predictive systems;
  • and translating technical findings into actionable, strategic recommendations for product development and executive stakeholders, ensuring the stability and profitability of global payment systems.

Telecommuting and/or working from home may be permissible pursuant to company policy. When not telecommuting, must report to work site.

What we offer you

  • A competitive salary and benefits
  • A variety of career development tools, resources and opportunities
  • The chance to work on some of the most challenging, relevant issues in the payment industry
  • Time to support charities and give back in your community



EEOC Statement

Worldpay is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here.

If you are made a conditional offer of employment and will be working in the United States, you will be required to undergo a drug test. In developing this job description care was taken to include all competencies and requirements needed to successfully perform the position. Reasonable accommodations will be provided for individuals with qualified disabilities both during the hiring process, as well as to allow the individual to perform the essential functions of the job, if hired.

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