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Reinforcement Learning Robotics Jobs in Ohio (NOW HIRING)

Statheros

Develop and train reinforcement learning models for real-world applications, focusing on efficiency ... Experience in applying PPO to [specific domain, e.g., robotics, gaming, finance, etc.

ACCEL Schools

Principal

Columbus, OH

The school offers a college prep curriculum enriched with STEM and Robotics. Capital Preparatory ... Delivering consistently high levels of achievement and learning for all students through rigorous ...

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Reinforcement Learning Robotics information

What are some common challenges faced when implementing reinforcement learning algorithms in robotics projects?

One common challenge in this role is bridging the gap between simulation and real-world environments, as algorithms that perform well in simulation may not translate directly to physical robots due to unpredictable variables and hardware limitations. Additionally, ensuring the safety and stability of the robot during training is crucial, since trial-and-error learning can sometimes result in unintended behaviors or hardware damage. Collaboration with hardware engineers and domain experts is often necessary to fine-tune models, interpret results, and iterate on solutions. Overcoming these challenges requires patience, adaptability, and strong communication skills within a multidisciplinary team.

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

To thrive as a Reinforcement Learning Robotics Engineer, you need a strong background in robotics, machine learning, and programming, typically supported by a degree in computer science, engineering, or a related field. Expertise with frameworks like TensorFlow or PyTorch, experience with simulation environments (such as Gazebo or ROS), and familiarity with reinforcement learning algorithms are essential. Strong problem-solving skills, creativity, and effective communication set standout professionals apart in this rapidly evolving field. These skills enable engineers to develop intelligent robotic systems that adapt and learn efficiently, driving innovation and practical deployment in real-world environments.

What is reinforcement learning in robotics?

Reinforcement learning in robotics refers to a type of machine learning where robots learn to perform tasks through trial and error, receiving feedback from their actions in the form of rewards or penalties. This approach allows robots to autonomously develop complex behaviors by interacting with their environment, rather than relying solely on pre-programmed instructions. Reinforcement learning is especially useful for tasks that are difficult to model explicitly, such as walking, grasping, or navigation. Over time, the robot improves its performance by maximizing the cumulative reward, leading to more efficient and adaptive behaviors.

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

AspectReinforcement Learning RoboticsMachine Learning Engineer
Required CredentialsDegree in Robotics, Computer Science, or related fields; knowledge of reinforcement learningDegree in Computer Science, Data Science, or related fields; expertise in machine learning algorithms
Work EnvironmentRobotics labs, manufacturing, autonomous systemsTech companies, data-driven projects, software development
Industry UsageAutonomous robots, industrial automation, researchData analysis, predictive modeling, AI applications

Reinforcement Learning Robotics focuses on applying reinforcement learning techniques to control and optimize robotic systems, often in physical environments. Machine Learning Engineers develop algorithms for a broad range of applications, including data analysis and predictive modeling. While both roles require knowledge of machine learning, Reinforcement Learning Robotics emphasizes robotics and real-world interaction, whereas Machine Learning Engineers work across various industries with software-based solutions.

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Reinforcement Learning Robotics jobs near you
Machine Learning Engineer, Robot Learning, Loco-Manipulation

Machine Learning Engineer, Robot Learning, Loco-Manipulation

Path Robotics

Columbus, OH • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 29 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.
We are standing up a new Robot Learning team focused on whole-body loco-manipulation for precision tasks in heavy manufacturing.
We are seeking a Machine Learning Engineer to join us as a founding member. You will be among the first ML engineers on a research stack that does not exist anywhere else in the field built around visual reasoning, learned action policies, and reinforcement-learning fine-tuning from real customer data.
What You'll Do
  • Build the team's robot-learning stack from the ground up. This is a founding role; you are designing the training infrastructure, data pipelines, simulation environments, model architectures, and deployment workflows - not inheriting them. Multi-modal perception, scene understanding, and learned action generation work in tight coordination on the stack you help create.
  • Stand up ML infrastructure - training pipelines, experiment tracking, data versioning, reproducible sim-to-real workflows.
  • Train policies across manipulation, locomotion, and the whole-body control coupling between them. On legged platforms performing precision tasks, manipulation and locomotion are not separable - every arm motion shifts the centre of mass; the whole-body controller compensates in real time to maintain accuracy at the tool. Behavioural cloning, diffusion- and flow-matching action generation, reinforcement-learning fine-tuning. Cobots, industrial arms, and mobile platforms.
  • Deploy in stages - through a phased rollout strategy that builds production trust over time. Every real-world execution accumulates training data for continuous improvement.
  • Collaborate daily with mechanical engineers, perception engineers, robotics engineers, and manufacturing domain experts. Within-department rotation across home teams is expected.

Who You Are
  • Ph.D. or Master's degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field - or equivalent experience.
  • 2+ years of hands-on robot learning experience. You have trained policies and deployed them on real robot hardware - not just in simulation.
  • Sim-to-real transfer experience - built simulation environments, implemented domain randomisation, transferred policies to physical robots, debugged where it broke.
  • Implementation experience with diffusion-based or flow-matching action policies for robots, and with action chunking.
  • Reinforcement learning for robotics applied on real hardware - sample-efficient on-robot methods, residual RL on top of pretrained policies, on-policy fine-tuning of foundation policies.
  • Strong programming skills in Python; PyTorch and ML training infrastructure at production level.
  • Practical experience with NVIDIA Isaac Sim / Isaac Lab, MuJoCo, or equivalent.
  • Comfort with physical robots - debugging, iterating, deploying.
  • Strong communication skills, able to convey complex technical concepts to a diverse audience.

Strongly Preferred:
  • Edge inference on edge-class hardware (TensorRT, ONNX, FP16 / INT8 quantisation). Real-time on-robot deployment is a core requirement.
  • Visual self-supervised representation learning experience on robot or 3D-vision tasks.
  • Legged-robot or whole-body control experience - locomotion, manipulation on a floating base, or the integration between them on quadrupeds or humanoids.
  • Physics-informed ML - hybrid models where learned components are constrained by known physics.
  • Experience building ML pipelines or infrastructure in a team setting.

Why You'll Love Working 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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