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Model Predictive Control Jobs in Ashburn, VA (NOW HIRING)

K2 Group, Inc.

CBRN Analyst

Arlington, VA · On-site

... Control and Communication (NC3) Division. Work will be performed onsite in the Arlington, VA area (Pentagon). Job Responsibilities: * Execute and refine predictive hazard models, data simulations ...

Amat

Global Trade Analyst

Washington, DC · On-site +1

$92K - $126K/yr

Apply predictive analytics and machine learning models to generate forward-looking insights ... Support development and standardization of SOPs, data governance frameworks, and control ...

steampunk

AI Developer with Security Clearance

Fairfax, VA · On-site

$140K - $190K/yr

Develop end-to-end AI solutions including LLM-powered applications, predictive ML models, multi ... Proficiency with Git and collaborative version control workflows * Ability to read and write REST ...

AEI

(Senior) Data Scientist

Washington, DC · On-site

Project-manage analyst workflows and provide supervision and quality control over analyst assignments. * Conduct advanced predictive modeling, statistical analyses, and machine learning on complex ...

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All JobsModel Predictive Control JobsModel Predictive Control Jobs in Ashburn, VA

Model Predictive Control information

See Ashburn, VA salary details

$56.2K

$98.8K

$134K

How much do model predictive control jobs pay per year?

As of Aug 9, 2026, the average yearly pay for model predictive control in Ashburn, VA is $98,757.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,400.00 and $110,400.00 per year, depending on experience, location, and employer.

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 is the difference between Model Predictive Control vs Control Systems Engineer?

AspectModel Predictive ControlControl Systems Engineer
CredentialsEngineering degree, control theory, process modelingEngineering degree, control systems, automation
Work EnvironmentIndustrial automation, process control, manufacturingDesign, develop, and maintain control systems across industries
Industry UsageProcess industries, chemical, oil & gas, manufacturingAutomation, 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 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 are popular job titles related to Model Predictive Control jobs in Ashburn, VA? For Model Predictive Control jobs in Ashburn, VA, the most frequently searched job titles are:
What job categories do people searching Model Predictive Control jobs in Ashburn, VA look for? The top searched job categories for Model Predictive Control jobs in Ashburn, VA are:
Model Predictive Control jobs near you
Infographic showing various Model Predictive Control job openings in Ashburn, VA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $98,757 per year, or $47.5 per hour.

CBRN Analyst with Security Clearance

K2 Group, Inc.

Arlington, VA • On-site

$101K - $201K/yr

Contractor

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 7 days ago

Job description

K2 Group, Inc. CBRN Analyst CBRNE/WMD-Defeat - Arlington, VA - Full Time *This is a contingent opportunity  K2 Group is seeking a CBRN Analyst to provide support to the Headquarters Air Force. This role provides specialized technical, analytical, and programmatic support for the Nuclear Command, Control and Communication (NC3) Division. Work will be performed onsite in the Arlington, VA area (Pentagon). Job Responsibilities:
* Execute and refine predictive hazard models, data simulations, and trend analysis within complex CBRN environments to anticipate adversarial threats. * Translate raw CBRN threat data and complex modeling outputs into clear, actionable intelligence and data-driven recommendations for senior leadership. * Conduct comprehensive risk assessments and provide real-time operational insights that directly align unit activities with Air Force CWMD policies and survival strategies. * Partner with cross-functional mission teams to track, monitor, and evaluate active environmental hazards, ensuring uninterrupted command and control. * Integrate established CBRN defense (CBRND) doctrine and modern hazard mitigation frameworks into local unit mission planning. * Design and validate tailored readiness exercises, leveraging operational data to stress-test and improve military response capabilities in contested environments. Qualifications Required: * Proven experience executing hazard prediction, environmental modeling, or tactical data analysis within CBRN environments. * Applied knowledge of CBRND operational doctrine, hazard mitigation frameworks, and risk assessment methodologies. * Demonstrated ability to synthesize complex, disparate technical data into concise operational insights that enhance military decision-making. * High School diploma or GED. Desired: * Experience delivering CBRN-specific operational analysis or specialized intelligence support tailored to Air Force programs or defense missions. * Practical proficiency utilizing geo-based analysis tools, such as ArcGIS, for predictive hazard mapping and terrain-based operational planning. * Proven success collaborating with multidisciplinary teams to plan, script, and execute tactical readiness validation exercises. * Bachelor’s degree in Environmental Science, Public Health, Military Science, or a related technical field. Clearance:
* Top Secret Clearance w/SCI is required Compensation:  The projected compensation range for this position is $101,100-$201,100.  There are differentiating factors that can impact a final salary rate, including but not limited to, Contract Wage Determination, relevant work experience, skills and competencies that align to the specified role, geographic location, education and certification as well as Federal Government Contract Labor categories.  In addition, K2 Group invests in its employees beyond just compensation.  Benefits:   K2 Group’s benefit offerings include:  Medical/ Dental/ Vision Insurance; FSA Medical & FSA Dependent Care; Pre-tax 401(k) & ROTH 401(k) plans; Profit Sharing Plan; Life & Accidental Death Insurance; Short Term/ Long Term Disability; Voluntary Group Life Insurance option; Tuition Reimbursement; Job-related Course Reimbursement; Holiday Pay; and Paid Time-Off.

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