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Reinforcement Learning Engineer Jobs in Kentucky

$180 - $240/hr

JOB SUMMARY The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep ...

New

$180 - $230/hr

Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the ...

New

$180 - $260/hr

... reinforcement learning, or other post-training techniques.- Strong software engineering fundamentals, including data structures, algorithms, systems design, and high-quality production code in Python ...

The role requires hands-on experience with various reinforcement learning methods and a deep ... linear programming, Markov decision processes, Actor-Critic methods, Offline RL methods (CQL ...

$180 - $240/hr

Every leading AI lab uses our datasets and reinforcement learning environments to encode and scale real-world expertise. We're hiring a Founding Machine Learning Engineer, Quality Intelligence to ...

$120 - $180/hr

Develop coding agents that understand, generate, and debug code across complex, multi-step programming tasks * Conduct research in reinforcement learning with a focus on RLHF, DPO, and PPO as ...

New

$179 - $269/hr

Engineering Group, Engineering Group > Machine Learning Researcher**General Summary:**Qualcomm AI ... LLMs and Multi-Modal Foundation Models, LLM Reasoning, Reinforcement Learning, Agentic AI and ...

New

$120 - $190/hr

The Platform engineer creates the tools, you create the decisions. Together you build something ... Reinforcement learning (RL) experience is a plus * Hyperparameter optimization (HPO) experience is ...

New

$180 - $250/hr

Whether leveraging reinforcement learning, imitation learning, teleoperation retargeting, or ... Software Engineering: Proficiency in Python , with experience building real-time robotic software ...

New

$350 - $850/hr

About the role As a Research Engineer on our team you will work end to end across the whole model ... History working with large scale reinforcement learning * Comfort with large scale data pipelines ...

New

$68 - $97/hr

... reinforcement learning, and system optimization, advancing recommendation systems beyond click ... We value original exploration and encourage research thinking and engineering practice equally.

New

$320 - $485/hr

Anthropic\'s RL Data team builds the systems that produce high-quality reinforcement learning data ... Strong software engineering skills in at least one modern programming language -- we mostly use ...

New

$190 - $210/hr

Experience in LLMs, agents, reinforcement learning, post-training workflows. * Ability to work ... Clear communication and strong cross-functional collaboration with researchers, engineers, and ...

New

$190 - $270/hr

Background in reinforcement learning and/or pretraining * Knowledge of containerization ... engineering, and scientific computing * Contributions to open-source scientific software or ...

New

$187 - $262/hr

Familiarity with concepts like prompt engineering, retrieval-augmented generation, function/tool calling, and evaluation of LLM-based systems. * Experience with reinforcement learning, bandits, or ...

$160 - $210/hr

Act as the technical authority for the Agentic AI roadmap, steering engineering pods from initial ... reinforcement learning, or data benchmark. Salary And Benefits We offer competitive salary and ...

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Reinforcement Learning Engineer information

See Kentucky salary details

$33K

$100.6K

$166.3K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 20, 2026, the average yearly pay for reinforcement learning engineer in Kentucky is $100,631.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,100.00 and $131,600.00 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 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 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 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 Kentucky?

For Reinforcement Learning Engineer jobs in Kentucky, the most frequently searched job titles are:

What cities in Kentucky are hiring for Reinforcement Learning Engineer jobs?

Cities in Kentucky with the most Reinforcement Learning Engineer job openings:

Infographic showing various Reinforcement Learning Engineer job openings in Kentucky as of August 2026, with employment types broken down into 92% Full Time, and 8% Contract. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $100,631 per year, or $48.4 per hour.

Senior Reinforcement Learning Engineer

Booster

On-site

$180 - $240/hr

Other

Posted 2 days ago

New


Job description

Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond.

We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.

JOB SUMMARY

The Senior Reinforcement Learning Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep expertise in RL to solve critical locomotion and manipulation challenges and deliver breakthrough results on physical hardware. The primary focus of this role is to rapidly implement, iterate, and deploy advanced learning algorithms to push the boundaries of what our robots can do. As a senior member of the team, this individual will also be responsible for mentoring junior engineers, elevating the team's overall technical capabilities through their guidance and expertise.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES
  • Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.
  • Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.
  • Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.
  • Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
  • Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.
  • Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.
  • Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.
SKILLS AND REQUIREMENTS
  • Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
  • Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
  • Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.
  • A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.
  • A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.
  • A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.
EDUCATION and/or EXPERIENCE
  • A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
  • A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.
  • Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
  • A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.
PHYSICAL REQUIREMENTS
  • Prolonged periods of sitting at a desk and working on a computer
  • Must be able to lift 15 pounds at times
  • Vision to read printed materials and a computer screen
  • Hearing and speech to communicate

Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

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