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

The Midtown Group

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Data Scientist

Rosslyn, VA · On-site

$60/hr

Use Azure DevOps and/or GitHub to support source control, CI/CD pipelines, and Agile development ... Experience developing predictive models, NLP solutions, dashboards, and investigative or analytical ...

Elder Research

Data Scientist

Arlington, VA · On-site

You will primarily create and deploy predictive models from a wide variety of data sources and ... Experience using version control (e.g. git, svn, Mercurial) and collaborative basic understanding ...

CACI

HCM Analytics & BI Developer

Reston, VA · On-site +1

... predictive analytics models, and enterprise data visualizations that shape human capital strategies ... of control Experience developing accessible analytics meeting Section 508 / WCAG standards ...

New

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 ...

Steampunk

AI Developer

Mclean, 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 ...

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 ...

Steampunk

AI Developer

Mclean, 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 ...

Axle

Associate Director of Data and Modeling

Rockville, MD

$60K - $60K/yr

At the same time, the role is larger than any single architecture, model, or platform. You will ... control so that data products remain understandable and trustworthy as programs evolve. * AI/ML and ...

CACI

Rotorcraft & Flight Test Engineer

Bethesda, MD · On-site

$79K - $162K/yr

... predictive modeling with real-world experimental testing Gain exposure to wind tunnel testing ... control theory Experience analyzing and reducing data, including data from drone flights ...

ANSER

Senior Computer Scientist

Reston, VA · On-site

... control repositories. • Employ data science techniques to support predictive analysis, social ... modeling, data mining, statistical analysis, and ML algorithms. • Experienced with knowledge ...

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Showing results 41-60

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 Sep 12, 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 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?

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 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 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 1% As Needed, 70% Full Time, 26% Part Time, 2% Contract, and 1% Nights. Highlights an 85% Physical, 1% Hybrid, and 14% Remote job distribution, with an average salary of $98,757 per year, or $47.5 per hour.

Data Scientist

Rosslyn, VA • On-site

The Midtown Group
Recruiting and Staffing Services • 11 - 50 employees
Learn more about The Midtown Groupexternal

$60/hr

Full-time

Medical, Dental, Vision, Retirement

Posted 15 days ago

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Key responsibilities

  • Develop, manage, and optimize automated data pipelines to support reliable analytics and data quality.

  • Extract, clean, transform, normalize, validate, and integrate data from various sources using appropriate tools and methods.

  • Develop, train, evaluate, deploy, and maintain machine learning and AI models.

Job description

We are seeking a Data Scientist to support a federal government client by applying advanced data analytics, machine learning, and artificial intelligence to complex datasets and mission-critical challenges. The Data Scientist will develop data pipelines, integrate diverse data sources, build analytical models, and translate complex findings into actionable insights.


Responsibilities

  • Develop, manage, and optimize automated data pipelines to support reliable analytics and data quality.
  • Extract, clean, transform, normalize, and validate structured and unstructured data.
  • Integrate data from flat files, relational databases, APIs, external systems, and other sources using JDBC/ODBC, REST APIs, and web scraping.
  • Develop data and analytics solutions using AWS and/or Azure, including Databricks, Azure Data Factory, and Azure Data Lake.
  • Use Azure DevOps and/or GitHub to support source control, CI/CD pipelines, and Agile development.
  • Apply Python, SQL, R, and/or JavaScript to develop datasets, models, dashboards, visualizations, and reports.
  • Apply advanced analytics including machine learning, AI, NLP, predictive analytics, statistical modeling, and data/text mining.
  • Develop, train, evaluate, deploy, and maintain machine learning and AI models.
  • Use PySpark/PySpark SQL to process and analyze large-scale datasets.
  • Identify trends, anomalies, patterns, and risks to support audit, investigative, oversight, and fraud, waste, and abuse activities.
  • Translate technical findings into clear narratives, recommendations, visualizations, and presentations.
  • Develop technical documentation, requirements, test plans, methodologies, and training materials.
  • Collaborate with technical teams, stakeholders, and management and provide guidance on data access, quality, storage, and analytics.


Qualifications

Required

  • US Citizenship required
  • 1+ year of experience working with AWS and/or Azure services, such as Databricks, Azure Data Factory, and Azure Data Lake.
  • Experience with Azure DevOps and/or GitHub, CI/CD pipelines, and Agile methodologies.
  • Experience developing and optimizing automated data pipelines and ETL/ELT processes.
  • Proficiency with Python and SQL; experience with R and/or JavaScript is a plus.
  • Experience working with structured and unstructured data and integrating multiple data sources.
  • Knowledge of machine learning, artificial intelligence, NLP, predictive analytics, statistical analysis, or data/text mining.
  • Strong analytical, problem-solving, technical writing, and communication skills.
  • Ability to work independently and effectively within a collaborative team environment.

Preferred

  • Experience with contract, procurement, and accounts payable (AP) data to identify trends, anomalies, and potential risks.
  • Experience using PySpark/PySpark SQL for large-scale data processing.
  • Experience with data lakes, data lakehouses, relational databases, and business intelligence tools.
  • Experience supporting federal audit, investigation, oversight, fraud, waste, or abuse-related activities.
  • Experience developing predictive models, NLP solutions, dashboards, and investigative or analytical visualizations.
  • Experience presenting technical findings, training materials, or conference presentations to large audiences.


The Midtown Group is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. We are a small, woman-owned business certified by the Women’s Business Enterprise National Council (WBENC). Operating from our headquarters in Washington, DC, we provide trusted staffing services nationwide. Our clients include thousands of the most prestigious Fortune 500 companies, law firms, financial organizations, tech innovators, non-profits, and lobbying firms, as well as federal, state and local government agencies.