Responsibilities : • Design, train, and deploy ML models for robotic control, quality prediction, and process optimization • Develop reinforcement learning and imitation learning systems for ...
Responsibilities : • Design, train, and deploy ML models for robotic control, quality prediction, and process optimization • Develop reinforcement learning and imitation learning systems for ...
... reinforcement learning, robotics and computer vision. Considerations: Exceptions to standard rates may apply to courses with unique credit hours, supervision, labs, clinical/practicum courses ...
... reinforcement learning, robotics and computer vision. Considerations: Exceptions to standard rates may apply to courses with unique credit hours, supervision, labs, clinical/practicum courses ...
... reinforcement learning, robotics and computer vision. Considerations: Exceptions to standard rates may apply to courses with unique credit hours, supervision, labs, clinical/practicum courses ...
... reinforcement learning, robotics and computer vision. Considerations: Exceptions to standard rates may apply to courses with unique credit hours, supervision, labs, clinical/practicum courses ...
... and robotics. RPL develops the open-source Modular Autonomous Discovery for Science (MADSci ... Generative models, reinforcement learning, and agent-based approaches to streamline experimentation ...
... and robotics. RPL develops the open-source Modular Autonomous Discovery for Science (MADSci ... Generative models, reinforcement learning, and agent-based approaches to streamline experimentation ...
... and robotics. RPL develops the open-source Modular Autonomous Discovery for Science (MADSci ... Generative models, reinforcement learning, and agent-based approaches to streamline experimentation ...
... and robotics. RPL develops the open-source Modular Autonomous Discovery for Science (MADSci ... Generative models, reinforcement learning, and agent-based approaches to streamline experimentation ...
AV Simulation Domain Expert (Sr. Principal) - US (Remote) or Chicago, IL
Chicago, IL · On-site +1
$170K - $250K/yr
Background in reinforcement learning, end-to-end driving systems, or multi-agent simulation. * Experience in automotive, robotics, or other safety-critical domains, including familiarity with ISO ...
AV Simulation Domain Expert (Sr. Principal) - US (Remote) or Chicago, IL
Chicago, IL · On-site +1
$170K - $250K/yr
Background in reinforcement learning, end-to-end driving systems, or multi-agent simulation. * Experience in automotive, robotics, or other safety-critical domains, including familiarity with ISO ...
Postdoctoral Appointee - Materials Informatics and Autonomous Synthesis
Lemont, IL · On-site
$72K - $121K/yr
... reinforcement learning for experiments, or uncertainty quantification * Experience with autonomous, self-driving, or robotic laboratory platforms * Background in electronic polymers, conjugated ...
Postdoctoral Appointee - Materials Informatics and Autonomous Synthesis
Lemont, IL · On-site
$72K - $121K/yr
... reinforcement learning for experiments, or uncertainty quantification * Experience with autonomous, self-driving, or robotic laboratory platforms * Background in electronic polymers, conjugated ...
... reinforcement learning for experiments, or uncertainty quantification * Experience with autonomous, self-driving, or robotic laboratory platforms * Background in electronic polymers, conjugated ...
... reinforcement learning for experiments, or uncertainty quantification * Experience with autonomous, self-driving, or robotic laboratory platforms * Background in electronic polymers, conjugated ...
Assistant Scientist - AI for Autonomous Synthesis and Multimodal Characterization
Lemont, IL · On-site
Laboratory automation and robotic synthesis platforms * Generative models, reinforcement learning, or agentic AI approaches for materials discovery and experiment planning * Multimodal data fusion ...
Assistant Scientist - AI for Autonomous Synthesis and Multimodal Characterization
Lemont, IL · On-site
Laboratory automation and robotic synthesis platforms * Generative models, reinforcement learning, or agentic AI approaches for materials discovery and experiment planning * Multimodal data fusion ...
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?
| Aspect | Reinforcement Learning Robotics | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Degree in Robotics, Computer Science, or related fields; knowledge of reinforcement learning | Degree in Computer Science, Data Science, or related fields; expertise in machine learning algorithms |
| Work Environment | Robotics labs, manufacturing, autonomous systems | Tech companies, data-driven projects, software development |
| Industry Usage | Autonomous robots, industrial automation, research | Data 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 Illinois? For Reinforcement Learning Robotics jobs in Illinois, the most frequently searched job titles are:
- Volunteer Junior Machine Learning Engineer
- Overnight First Robotics
- Machine Learning Infrastructure Engineer
- Remote Biomechanical Engineer
- Flexible Schedule No Experience Robotics Engineer
- Machine Learning Ai Developer
- Manager Remote Machine Learning Engineer
- Machine Learning Engineer Biotech
- Urgently Hiring Machine Learning Engineer New Grad
- Remote Contract Robotics Engineer
What job categories do people searching Reinforcement Learning Robotics jobs in Illinois look for? The top searched job categories for Reinforcement Learning Robotics jobs in Illinois are:
What cities in Illinois are hiring for Reinforcement Learning Robotics jobs? Cities in Illinois with the most Reinforcement Learning Robotics job openings:
Job description
Job Summary:
LightSpeed Build Technologies is revolutionizing the construction industry through AI-powered robotics. As an AI & Machine Learning Engineer, you will design, build, and deploy intelligent systems for construction robots, focusing on machine learning models for computer vision, predictive analytics, and process optimization.
Responsibilities:
• Design, train, and deploy ML models for robotic control, quality prediction, and process optimization
• Develop reinforcement learning and imitation learning systems for robot task planning
• Build predictive maintenance models using sensor data to anticipate equipment failures
• Implement anomaly detection for real-time quality monitoring during automated assembly
• Optimize model inference for edge deployment on GPU-accelerated hardware in production
• Develop deep learning pipelines for object detection, segmentation, and pose estimation
• Build real-time vision systems for robotic guidance, workpiece tracking, and dimensional verification
• Implement 3D point cloud processing for construction material recognition
• Design and train models for visual quality inspection using depth cameras and industrial imaging
• Build ML data pipelines from sensor acquisition through model training and deployment
• Establish data labeling, versioning, and management workflows for training datasets
• Implement model monitoring, A/B testing, and continuous improvement in production
• Design experiment tracking and reproducibility infrastructure (MLflow, Weights & Biases)
• Integrate ML models with ROS2-based robot control for real-time inference
• Optimize models for NVIDIA Jetson, industrial PCs, and edge computing platforms
• Collaborate with robotics engineers on sensor selection, placement, and calibration
• Support scaling ML systems across multiple production cells and sites
Qualifications:
Required:
• 4+ years hands-on ML engineering building and deploying production models
• Deep proficiency with PyTorch or TensorFlow for model development and training
• Strong computer vision experience: object detection, segmentation, depth estimation, or 3D vision
• Understanding of reinforcement learning, imitation learning, or robot learning approaches
• Experience optimizing ML models for edge deployment (TensorRT, ONNX, quantization)
• Strong Python with experience in C++ for performance-critical components
• Experience with ML infrastructure: data pipelines, experiment tracking, model serving
• Proficiency with Linux, Docker, Git, and CI/CD workflows
• Understanding of real-time system constraints for ML inference in production
Preferred:
• MS or PhD in Machine Learning, Computer Science, Robotics, or related field
• Experience with robotics simulation: MuJoCo, IsaacSIM, or similar
• Background in manufacturing, industrial automation, or construction technology
• Experience with ROS/ROS2 integration for ML-powered robotics
• Published research or patents in computer vision, robot learning, or related ML
• Experience with NVIDIA ecosystem: CUDA, cuDNN, TensorRT, Jetson platforms
Company:
BUILDING TOMORROW'S HOMES, FASTER AND SMARTER The Lightspeed Integrated Walls, Floors and Roof Systems are built with advanced software and AI driven industrial robots, allowing us to seamlessly craft the walls, floors and roofs, integrating the framing, MEPs, insulation, and drywall in a single, efficient manufacturing line. Founded in , the company is headquartered in , , with a team of 11-50 employees. The company is currently Early Stage.
LightSpeed Build Technologies is revolutionizing the construction industry through AI-powered robotics. As an AI & Machine Learning Engineer, you will design, build, and deploy intelligent systems for construction robots, focusing on machine learning models for computer vision, predictive analytics, and process optimization.
Responsibilities:
• Design, train, and deploy ML models for robotic control, quality prediction, and process optimization
• Develop reinforcement learning and imitation learning systems for robot task planning
• Build predictive maintenance models using sensor data to anticipate equipment failures
• Implement anomaly detection for real-time quality monitoring during automated assembly
• Optimize model inference for edge deployment on GPU-accelerated hardware in production
• Develop deep learning pipelines for object detection, segmentation, and pose estimation
• Build real-time vision systems for robotic guidance, workpiece tracking, and dimensional verification
• Implement 3D point cloud processing for construction material recognition
• Design and train models for visual quality inspection using depth cameras and industrial imaging
• Build ML data pipelines from sensor acquisition through model training and deployment
• Establish data labeling, versioning, and management workflows for training datasets
• Implement model monitoring, A/B testing, and continuous improvement in production
• Design experiment tracking and reproducibility infrastructure (MLflow, Weights & Biases)
• Integrate ML models with ROS2-based robot control for real-time inference
• Optimize models for NVIDIA Jetson, industrial PCs, and edge computing platforms
• Collaborate with robotics engineers on sensor selection, placement, and calibration
• Support scaling ML systems across multiple production cells and sites
Qualifications:
Required:
• 4+ years hands-on ML engineering building and deploying production models
• Deep proficiency with PyTorch or TensorFlow for model development and training
• Strong computer vision experience: object detection, segmentation, depth estimation, or 3D vision
• Understanding of reinforcement learning, imitation learning, or robot learning approaches
• Experience optimizing ML models for edge deployment (TensorRT, ONNX, quantization)
• Strong Python with experience in C++ for performance-critical components
• Experience with ML infrastructure: data pipelines, experiment tracking, model serving
• Proficiency with Linux, Docker, Git, and CI/CD workflows
• Understanding of real-time system constraints for ML inference in production
Preferred:
• MS or PhD in Machine Learning, Computer Science, Robotics, or related field
• Experience with robotics simulation: MuJoCo, IsaacSIM, or similar
• Background in manufacturing, industrial automation, or construction technology
• Experience with ROS/ROS2 integration for ML-powered robotics
• Published research or patents in computer vision, robot learning, or related ML
• Experience with NVIDIA ecosystem: CUDA, cuDNN, TensorRT, Jetson platforms
Company:
BUILDING TOMORROW'S HOMES, FASTER AND SMARTER The Lightspeed Integrated Walls, Floors and Roof Systems are built with advanced software and AI driven industrial robots, allowing us to seamlessly craft the walls, floors and roofs, integrating the framing, MEPs, insulation, and drywall in a single, efficient manufacturing line. Founded in , the company is headquartered in , , with a team of 11-50 employees. The company is currently Early Stage.