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Reinforcement Learning Engineer Jobs in Ohio (NOW HIRING)

Path Robotics

Senior Machine Learning Engineer

Columbus, OH · On-site +1

$100K - $138K/yr

Senior Machine Learning Engineer, Reinforcement Learning Senior ML Engineer, World Models What You'll Do * Build action-conditioned world models that predict how the welding process evolves under ...

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

Senior Software Engineer

Dayton, OH · On-site +1

$134K - $184K/yr

Reinforcement learning * Agentic AI * Experience programming for embedded and physical devices * Multi-agent coordination of UxVs * MAVLINK or other C2 protocols * ROS * TAK * DevSecOps and CI/CD ...

STR

Autonomy Algorithms Senior Software Engineer

Dayton, OH · On-site +1

$134K - $184K/yr

Reinforcement learning * Agentic AI * Experience programming for embedded and physical devices * Multi-agent coordination of UxVs * MAVLINK or other C2 protocols * ROS TAK * DevSecOps and CI/CD tool ...

Booz Allen Hamilton

Senior AI/ML Engineer

Dayton, OH · On-site

$99K - $225K/yr

R0242678 AI/ML Engineer, Senior The Opportunity: As a Senior Artificial Intelligence and Machine ... reinforcement learning * 5+ years of experience developing AI / ML capabilities to operate in a ...

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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 Aug 4, 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 is a reinforcement learning engineer?

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 72% Full Time, and 28% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $110,152 per year, or $53 per hour.

Senior Machine Learning Engineer

Path Robotics

Columbus, OH • On-site, Remote

$100K - $138K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 21 days ago

Job description

Build the Path Forward
At Path Robotics, we're building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.
Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together.
Manufacturing demands exceptionally high performance, reliability, and adaptability. Processes like welding involve fast, complex, and poorly modeled physics that traditional simulators struggle to capture - especially in the long tail of real-world conditions.
We are building intelligent robotic systems that learn directly from data by combining neural world models with reinforcement learning. Our goal is to give robots the ability to learn, predict, and plan in complex manufacturing environments by replacing or augmenting classical physics simulators with fast, high-fidelity learned ones.
We are seeking a Senior Machine Learning Engineer to lead the development of a neural welding simulator - a learned world model that captures the visual and physical dynamics of welding and enables large-scale RL training. This role sits at the intersection of generative modeling, robotics, and applied physics. It is research-heavy by design, while still grounded in production reality.
We are hiring two Senior Machine Learning Engineers with complementary specializations:
  • Senior Machine Learning Engineer, World Models
  • Senior Machine Learning Engineer, Reinforcement Learning
Senior ML Engineer, World Models
What You'll Do
  • Build action-conditioned world models that predict how the welding process evolves under changes to robot motion and process parameters.
  • Model relationships among inputs, system state, physical dynamics, and resulting weld quality.
  • Develop multimodal models using data such as video, 3D scans, thermal measurements, electrical signals, robot state, and process parameters.
  • Explore latent dynamics, video prediction, generative modeling, and spatiotemporal representations.
  • Improve long-horizon rollout accuracy, physical plausibility, temporal consistency, and computational efficiency.
  • Quantify model uncertainty and identify conditions under which predictions are unreliable.
  • Validate learned predictions against real-world welding data.
  • Integrate the model into RL, planning, process-optimization, evaluation, and synthetic-data workflows.
  • Prevent downstream optimization systems from exploiting inaccuracies in the learned model.
  • Translate promising research into scalable training and inference systems.
Senior ML Engineer, Reinforcement Learning
What You'll Do
  • Develop reinforcement learning approaches for optimizing welding decisions and process outcomes.
  • Define state, observation, action, and reward representations based on measurable manufacturing objectives.
  • Train and evaluate policies using learned world models, traditional simulation, offline datasets, and controlled real-world experiments.
  • Develop offline, model-based, or constrained RL methods suitable for limited and expensive physical interaction.
  • Optimize across competing objectives such as weld quality, cycle time, reliability, energy use, and equipment constraints.
  • Design methods that account for uncertainty, distribution shift, delayed outcomes, and sparse or imperfect reward signals.
  • Diagnose reward exploitation, unsafe behavior, policy instability, and model exploitation.
  • Establish reliable offline and real-world policy evaluation methods.
  • Partner with controls, welding, robotics, world-model, data, and ML infrastructure engineers.
  • Translate research prototypes into dependable training, evaluation, and deployment systems.
Who You Are
  • Master's or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practical experience.
  • Experience developing and deploying reinforcement learning algorithms on real-world systems.
  • Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
  • Experience with simulation environments (e.g., MuJoCo, Isaac Gym).
  • Solid understanding of probability, statistics, and optimization.
  • Experience with training and deploying ML models in production systems.
Why You'll Love It Here
  • Daily free lunch to keep you fueled and connected with the team
  • Flexible PTO so you can take the time you need, when you need it
  • Comprehensive medical, dental, and vision coverage
  • 6 weeks fully paid parental leave, plus an additional 6-8 weeks for birthing parents (12-14 weeks total)
  • 401(k) retirement plan through Empower
  • Generous employee referral bonuses-help us grow our team!
Who We Are
At Path Robotics we love coming to work to solve interesting and tough challenges but also because our ideas are welcomed and valued. We encourage unique thinking and are dedicated to creating a diverse and inclusive environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
If you require a reasonable accommodation to participate in the application process or any part of the hiring process, please contact HR@path-robotics.com. We are committed to providing equal access and will work with qualified individuals to ensure a fair and accessible hiring experience. We will respond to your request within 48 hours.

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