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Fulltime Reinforcement Learning Phd Jobs in Ohio

Penske

Staff Data Scientist

Beachwood, OH · On-site

... AI, and reinforcement learning where applicable. * Oversee the full model lifecycle: data ... PhD preferred in Engineering, Operations Research, Statistics, Applied Math, Computer Science, Data ...

STR

Lead Algorithm & Signal Processing Engineer

Dayton, OH · On-site

$157K - $220K/yr

... reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network ... Pay Information Full-Time Salary Range: $157,000 - $220,000 The salary range listed is based on ...

Penske

Staff Data Scientist

Beachwood, OH · On-site +1

... AI, and reinforcement learning where applicable. * Oversee the full model lifecycle: data ... PhD preferred in Engineering, Operations Research, Statistics, Applied Math, Computer Science, Data ...

Penske

Staff Data Scientist

Beachwood, OH · On-site +1

... AI, and reinforcement learning where applicable. * Oversee the full model lifecycle: data ... PhD preferred in Engineering, Operations Research, Statistics, Applied Math, Computer Science, Data ...

STR

Lead Algorithm & Signal Processing Engineer

Dayton, OH · On-site

$157K - $220K/yr

... reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network ... Pay Information Full-Time Salary Range: $157,000 - $220,000 The salary range listed is based on ...

Proctor & Gamble

Data Scientist

Cincinnati, OH · On-site

$85K - $122K/yr

... and reinforcement learning), you will directly contribute to the development of scalable Data ... Job Schedule Full time Job Number R000135859 Job Segmentation Entry Level Starting Pay / Salary ...

Medpace, Inc.

PhD Medical Physicist

Cincinnati, OH · On-site +1

Job Summary Our imaging services are growing rapidly, and we are currently seeking a full-time, ... Ongoing training and continuous learning opportunities throughout your career * An environment that ...

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Fulltime Reinforcement Learning Phd information

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

AspectFulltime Reinforcement Learning PhdMachine Learning Engineer
Required CredentialsPhD in Computer Science, AI, or related fieldBachelor's or Master's in CS, AI, or related field
Work EnvironmentResearch-focused, academic or R&D labsIndustry, product development teams
Employer & Industry UsageUniversities, research institutions, tech companiesTech companies, startups, enterprise firms
Common Search & ComparisonYesNo

Fulltime Reinforcement Learning Phds typically focus on research and theoretical development in AI, often working in academic or R&D settings. Machine Learning Engineers apply AI techniques to develop practical applications in industry. While both roles require strong AI knowledge, the Phd emphasizes research, whereas the Engineer emphasizes implementation.

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Cities in Ohio with the most Fulltime Reinforcement Learning Phd job openings:

Fulltime Reinforcement Learning Phd jobs near you
Infographic showing various Fulltime Reinforcement Learning Phd job openings in Ohio as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 18% Part Time, 1% Temporary, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Senior Machine Learning Engineer (Reinforcement Learning/World Model)

Path Robotics

Columbus, OH • On-site, Remote

$100K - $138K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 16 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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