... models for computer vision, predictive analytics, and process optimization. Responsibilities : • Design, train, and deploy ML models for robotic control, quality prediction, and process ...
... models for computer vision, predictive analytics, and process optimization. Responsibilities : • Design, train, and deploy ML models for robotic control, quality prediction, and process ...
Staff Data Scientist, ML (People Analytics & Insights)
Chicago, IL · On-site
$217 - $255/hr
This is a unique opportunity to apply state‑of‑the‑art language models and predictive ... Drive data access control and governance: Build the frameworks for role‑based access controls and ...
Staff Data Scientist, ML (People Analytics & Insights)
Chicago, IL · On-site
$217 - $255/hr
This is a unique opportunity to apply state‑of‑the‑art language models and predictive ... Drive data access control and governance: Build the frameworks for role‑based access controls and ...
Staff Data Scientist, ML (People Analytics & Insights)
Chicago, IL · On-site
$170 - $255/hr
This is a unique opportunity to apply state‑of‑the‑art language models and predictive ... Drive data access control and governance: Build the frameworks for role‑based access controls and ...
Staff Data Scientist, ML (People Analytics & Insights)
Chicago, IL · On-site
$170 - $255/hr
This is a unique opportunity to apply state‑of‑the‑art language models and predictive ... Drive data access control and governance: Build the frameworks for role‑based access controls and ...
AI & Machine Learning Engineer
Northbrook, IL · On-site
$100 - $130/hr
... deploy ML models for robotic control, quality prediction, and process optimization * Develop reinforcement learning and imitation learning systems for robot task planning * Build predictive ...
AI & Machine Learning Engineer
Northbrook, IL · On-site
$100 - $130/hr
... deploy ML models for robotic control, quality prediction, and process optimization * Develop reinforcement learning and imitation learning systems for robot task planning * Build predictive ...
Senior Scientist II, Biologics Drug Product Development
North Chicago, IL · On-site
$90 - $130/hr
Experience with quality risk management and drug product control strategies. Key Competencies * Deep scientific knowledge and significant hands‑on experience with predictive stability modeling ...
Senior Scientist II, Biologics Drug Product Development
North Chicago, IL · On-site
$90 - $130/hr
Experience with quality risk management and drug product control strategies. Key Competencies * Deep scientific knowledge and significant hands‑on experience with predictive stability modeling ...
This is a unique opportunity to apply state-of-the-art language models and predictive analytics to ... Drive data access control and governance: Build the frameworks for role-based access controls and ...
This is a unique opportunity to apply state-of-the-art language models and predictive analytics to ... Drive data access control and governance: Build the frameworks for role-based access controls and ...
Lead Data & AI Engineer
Chicago, IL · Hybrid
$112K - $135K/yr
Proven ability to deploy predictive models and analytics solutions into production environments ... control. * Experience working with industrial IoT, SCADA, and MES systems, including real-time data ...
Lead Data & AI Engineer
Chicago, IL · Hybrid
$112K - $135K/yr
Proven ability to deploy predictive models and analytics solutions into production environments ... control. * Experience working with industrial IoT, SCADA, and MES systems, including real-time data ...
Lead Data & AI Engineer
Chicago, IL · On-site
$112K - $135K/yr
Proven ability to deploy predictive models and analytics solutions into production environments ... control. * Experience working with industrial IoT, SCADA, and MES systems, including real-time data ...
Lead Data & AI Engineer
Chicago, IL · On-site
$112K - $135K/yr
Proven ability to deploy predictive models and analytics solutions into production environments ... control. * Experience working with industrial IoT, SCADA, and MES systems, including real-time data ...
AVP, Machine Learning & Modeling
$156K - $290K/yr
... predictive analytics, and optimization. Model Development and Deployment Oversee the design ... control, validation, and ongoing performance monitoring. Partner with risk management and ...
AVP, Machine Learning & Modeling
$156K - $290K/yr
... predictive analytics, and optimization. Model Development and Deployment Oversee the design ... control, validation, and ongoing performance monitoring. Partner with risk management and ...
Design, build, and deploy predictive and prescriptive models that support process robustness assessment, control strategy optimization, and commercial process validation across late-stage biologics ...
Quick apply
Design, build, and deploy predictive and prescriptive models that support process robustness assessment, control strategy optimization, and commercial process validation across late-stage biologics ...
Scientific Technical Lead, Late Stage CMC
North Chicago, IL · On-site
$150 - $230/hr
Design, build, and deploy predictive and prescriptive models that support process robustness assessment, control strategy optimization, and commercial process validation across late-stage biologics ...
Scientific Technical Lead, Late Stage CMC
North Chicago, IL · On-site
$150 - $230/hr
Design, build, and deploy predictive and prescriptive models that support process robustness assessment, control strategy optimization, and commercial process validation across late-stage biologics ...
Scientific Technical Lead, Late Stage CMC
North Chicago, IL · On-site
$109K/yr
Design, build, and deploy predictive and prescriptive models that support process robustness assessment, control strategy optimization, and commercial process validation across late-stage biologics ...
Scientific Technical Lead, Late Stage CMC
North Chicago, IL · On-site
$109K/yr
Design, build, and deploy predictive and prescriptive models that support process robustness assessment, control strategy optimization, and commercial process validation across late-stage biologics ...
Design, build, and deploy predictive and prescriptive models that support process robustness assessment, control strategy optimization, and commercial process validation across late-stage biologics ...
Design, build, and deploy predictive and prescriptive models that support process robustness assessment, control strategy optimization, and commercial process validation across late-stage biologics ...
Experience with quality risk management and drug product control strategies. Key Competencies: * Deep scientific knowledge and significant hands-on experience with predictive stability modeling ...
Experience with quality risk management and drug product control strategies. Key Competencies: * Deep scientific knowledge and significant hands-on experience with predictive stability modeling ...
Experience with quality risk management and drug product control strategies. Key Competencies: * Deep scientific knowledge and significant hands-on experience with predictive stability modeling ...
Quick apply
Experience with quality risk management and drug product control strategies. Key Competencies: * Deep scientific knowledge and significant hands-on experience with predictive stability modeling ...
Experience with quality risk management and drug product control strategies. Key Competencies: * Deep scientific knowledge and significant hands-on experience with predictive stability modeling ...
Experience with quality risk management and drug product control strategies. Key Competencies: * Deep scientific knowledge and significant hands-on experience with predictive stability modeling ...
... predictive models used to calibrate raters, and provide oversight on non-code pipelines (e.g ... control, testing, code review and reproducible pipelines). * Experience in using cloud platforms ...
... predictive models used to calibrate raters, and provide oversight on non-code pipelines (e.g ... control, testing, code review and reproducible pipelines). * Experience in using cloud platforms ...
... predictive models used to calibrate raters, and provide oversight on non-code pipelines (e.g ... control, testing, code review and reproducible pipelines). * Experience in using cloud platforms ...
... predictive models used to calibrate raters, and provide oversight on non-code pipelines (e.g ... control, testing, code review and reproducible pipelines). * Experience in using cloud platforms ...
Sr P&C Data Scientist
Chicago, IL · On-site
... control * Follow model development best practices and model risk governance standards Business ... Familiarity with several predictive modeling techniques (e.g., GLMs, treebased models, gradient ...
Sr P&C Data Scientist
Chicago, IL · On-site
... control * Follow model development best practices and model risk governance standards Business ... Familiarity with several predictive modeling techniques (e.g., GLMs, treebased models, gradient ...
... predictive modeling, stratification, explainable AI, and integrative multimodal analysis. The ... control, high-performance computing, and open science workflows * Strong quantitative skills as ...
New
... predictive modeling, stratification, explainable AI, and integrative multimodal analysis. The ... control, high-performance computing, and open science workflows * Strong quantitative skills as ...
New
Model Predictive Control information
What is model predictive control?
Model Predictive Control (MPC) is an advanced method of process control that uses a mathematical model to predict and optimize the future behavior of a system. It works by solving an optimization problem at each control step to determine the best sequence of control actions, taking into account system constraints and objectives. MPC is widely used in industries such as chemical processing, energy, and automotive because it can handle multivariable control problems and anticipate future events. Its predictive nature allows for improved performance, stability, and efficiency compared to traditional control methods.
What are the typical challenges faced by engineers working with model predictive control systems in an industrial setting?
Engineers working with Model Predictive Control systems often encounter challenges related to model accuracy, computational demands, and real-time implementation. Ensuring the process model accurately represents the plant dynamics is critical, as discrepancies can lead to suboptimal control performance. Additionally, MPC algorithms can be computationally intensive, particularly for large-scale or fast processes, requiring careful tuning and optimization to maintain real-time operation. Collaboration with process engineers and IT specialists is common, as integrating MPC with existing control systems and plant infrastructure is a key part of the role.
What are the key skills and qualifications needed to thrive as a model predictive control engineer, and why are they important?
To thrive as a Model Predictive Control Engineer, you need strong foundations in control theory, applied mathematics, and process engineering, usually supported by a degree in engineering or a related field. Proficiency with simulation tools such as MATLAB/Simulink, programming languages like Python or C++, and familiarity with industrial automation systems are typically required. Analytical thinking, problem-solving abilities, and effective communication skills help distinguish top performers in this role. These skills are essential for designing, implementing, and optimizing advanced control algorithms that improve system performance and reliability in complex industrial environments.
What is the difference between Model Predictive Control vs Control Systems Engineer?
| Aspect | Model Predictive Control | Control Systems Engineer |
|---|---|---|
| Credentials | Engineering degree, control theory, process modeling | Engineering degree, control systems, automation |
| Work Environment | Industrial automation, process control, manufacturing | Design, develop, and maintain control systems across industries |
| Industry Usage | Process industries, chemical, oil & gas, manufacturing | Automation, robotics, embedded systems, industrial sectors |
Model Predictive Control (MPC) focuses on advanced control algorithms for optimizing processes, while Control Systems Engineers design and implement various control systems. MPC is a specialized skill within control engineering, often requiring knowledge of process modeling and optimization, whereas Control Systems Engineers have broader responsibilities across multiple control technologies. Both roles are essential in industrial automation but differ in scope and application.
What does a model predictive control do?
A Model Predictive Control (MPC) engineer designs control systems that use a mathematical model to predict future system behavior and optimize control actions accordingly. MPC is commonly used in industries like process control and robotics, requiring skills in control theory, programming, and system modeling. The role involves developing algorithms, tuning controllers, and ensuring system stability and efficiency.
What are popular job titles related to Model Predictive Control jobs in Illinois?
For Model Predictive Control jobs in Illinois, the most frequently searched job titles are:
What job categories do people searching Model Predictive Control jobs in Illinois look for?
The top searched job categories for Model Predictive Control jobs in Illinois are:
What cities in Illinois are hiring for Model Predictive Control jobs?
Cities in Illinois with the most Model Predictive Control 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.