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Reinforcement Learning Robotics Jobs in Kentucky

$281 - $421/hr

... such as reinforcement learning to achieve robust, adaptive behavior. * Cross-Functional ... Significant background in robotics technologies related to motion planning, behavior modeling ...

New

$220 - $292/hr

ABOUT THE TEAM The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic ... reinforcement learning alignment loops (RLHF/DPO) to guarantee model safety and predictability in ...

$196 - $294/hr

... such as reinforcement learning to achieve robust, adaptive behavior. * Cross-Functional ... Significant background in one or several robotic technology areas related to control systems ...

New

$170 - $210/hr

Background in robotics, autonomous vehicles, or drone systems * Experience with sensor fusion or ... reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The ...

Showing results 21-24

Reinforcement Learning Robotics information

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 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 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.

What are popular job titles related to Reinforcement Learning Robotics jobs in Kentucky?

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

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

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

Senior Staff Software Engineer, Autonomy Capabilities (R4587)

Shield AI

On-site

$281 - $421/hr

Other

Posted 2 days ago

New


Job description

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

The Autonomy Capabilities Team develops functionality to automate single- and multi-agent teams of platforms in pursuit of mission objectives and acts as a central discipline-aligned focal-point for encouraging consistent usage of technical approaches across business portfolios. This team operates at all levels of the command-and-control hierarchy (from advanced control laws, through motion planning, and up to advanced tactical behaviors and multi-agent coordination) and across a multitude of mission sets, platform types, and operational domains (e.g., air, sea, space). The team puts a strong emphasis on both fundamentals (e.g., aircraft kinematics/dynamics, trajectory design, optimization, information fusion, efficient algorithms, etc.) and software integration skillsets (e.g. structures, protocols, threading, interface management, etc.) to bring market-differentiating capabilities to customers that seamlessly operate within their integration contexts.

This position is perfect for an individual who enjoys solving complex problems across a diverse set of programs and integration contexts. An ideal candidate is expected to address operational system needs through a multitude of advanced methodologies that blend traditional control system approaches with advanced optimization. Developed solutions are expected to be integrated into real-world platforms with near-term program impacts and rewards and so balancing theory with practice and rigorous implementation is paramount.

What you'll do:
  • Tactical Autonomy Design – Design tactical autonomy algorithms to enable unmanned aircraft to perform complex missions across air, land, and sea domains with minimal human supervision.
  • High-Performance Software Development – Develop high-performance software modules that incorporate planning, decision-making, and behavior execution strategies for dynamic and adversarial environments.
  • Behavior Architecture Implementation – Implement and test behavior architectures that enable multi-agent coordination, target engagement, reconnaissance, and survivability in contested scenarios.
  • Hybrid Autonomy Integration – Work at the intersection of classical autonomy and machine learning, blending rule-based systems with learning-based methods such as reinforcement learning to achieve robust, adaptive behavior.
  • Cross-Functional Collaboration – Collaborate with cross-functional teams including perception, planning, simulation, hardware, and flight test to ensure seamless integration of autonomy solutions on real-world platforms.
  • Deployment & Field Testing – Deploy autonomy capabilities to real platforms and participate in field tests and flight demos, validating performance in operationally relevant conditions.
  • Mission Data Analysis – Analyze mission logs and performance data to diagnose failures, optimize behavior models, and inform iterative development.
  • R&D and Roadmapping – Contribute to the autonomy roadmap by researching and prototyping new algorithms, identifying tactical capability gaps, and proposing novel solutions that advance Shield AI’s mission.
  • Program Support & Adaptation – Support defense-focused programs and customer needs by adapting autonomy solutions to evolving mission sets, compliance requirements, and operational feedback.
  • Travel Requirement – Members of this team typically travel around 10-15% of the year (to different office locations, customer sites, and flight integration events).
Required Qualifications:
  • BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience
  • Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 7 years with a PhD; or equivalent work experience.
  • Proficiency in programming languages such as C++ and Python, and familiarity with real-time operating systems (RTOS).
  • Significant background in robotics technologies related to motion planning, behavior modeling, decision-making, or autonomous system design.
  • Significant experience with unmanned system technologies and accompanying algorithms (specifically air domain)
  • Experience with simulation tools and environments (e.g., AFSIM, NGTS) for testing and validation.
  • Strong problem-solving skills, with the ability to troubleshoot and optimize system performance.
  • Excellent communication and teamwork skills, with the ability to work effectively in a collaborative, multidisciplinary environment.
  • Ability to obtain a SECRET clearance.
Preferred Qualifications:
  • Experience applying ML/RL techniques in autonomy pipelines.
  • Background in collaborative behaviors, swarm robotics, or distributed decision-making.
  • Familiarity with tactical behaviors for unmanned systems in DoD or government programs.
  • Work on behaviors applicable across air, ground, and maritime vehicles.
  • Hands-on experience supporting flight demos or live exercises.
  • Experience with UCI and OMS Standards

$280,512 - $420,768 a year

Full-time regular employee offer package:

Pay within range listed + Bonus + Benefits + Equity

Temporary employee offer package:

Pay within range listed above + temporary benefits package (applicable after 60 days of employment)

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.

Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed toequal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.

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