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Reinforcement Learning Engineer Jobs in Ohio (NOW HIRING)

Statheros

We are looking for a talented AI Engineer specializing in Proximal Policy Optimization (PPO) to ... Develop and train reinforcement learning models for real-world applications, focusing on efficiency ...

STR

Lead Algorithm & Signal Processing Engineer

Dayton, OH · On-site

$157K - $220K/yr

We investigate cutting edge technologies and interface with engineers across sectors and divisions ... reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network ...

STR

Lead Algorithm & Signal Processing Engineer

Dayton, OH · On-site

$157K - $220K/yr

We investigate cutting edge technologies and interface with engineers across sectors and divisions ... reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network ...

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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 Jun 22, 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 June 2026, with employment types broken down into 98% Full Time, and 2% Part Time. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $110,152 per year, or $53 per hour.
Machine Learning Engineer

Machine Learning Engineer

Apex Informatics

Cincinnati, OH • On-site

Full-time

Posted 9 days ago

Job description

Below is my newest requirement. Please send Full Legal Name, LinkedIn, Location, Contact Details, C2C rate, and work authorization status with each submittal.
Client: Kroger
Location: Hybrid onsite in Cincinnati OH (local only)
Interview Mode: Virtual Interview
Type: Contract
Work authorization: Cannot work with OPT or CPT
Rate: Open (market rate)
We are seeking a dynamic Senior Machine Learning Engineer to lead the integration and operationalization of machine learning models. This role requires collaboration with data scientists and leadership teams, and a strong foundation in MLOps methodologies. Experience in diverse ML platforms, including Google Vertex AI and other cloud and open-source technologies, is essential. The candidate will bridge MLOps, data science, and leadership to ensure the smooth functioning of our ML infrastructure.
Qualifications:
Minimum of 4 years of experience in MLOps, with a demonstrated ability to work with various ML platforms.
Strong proficiency in Python and familiarity with data science methodologies.
Experience with cloud technologies, particularly Google Cloud and Vertex AI, and adaptability to technologies like Microsoft Azure or open-source tools.
Excellent communication skills, capable of bridging technical and business domains
Experience in developing state-of-the-art techniques for multi-stage, personalized, context-aware, and sequential recommender systems.
Hands-on experience working on recommender systems, drawing from ML techniques such as embedding based retrieval, reinforcement learning, transformers, and LLMs.
Capable software engineering skills to lead a multi stage recommender system model lifecycle from inception to production.

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