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Model Predictive Control Jobs in Upland, CA (NOW HIRING)

Develop predictive models for areas such as revenue, expenses, headcount, bookings, customer ... CD, code review, and version control * Experience working in a large, global, or matrixed ...

DATA SCIENTIST II

Norco, CA · On-site

$115K - $130K/yr

Develop, implement, and maintain predictive models and machine learning algorithms * Collaborate ... Experience with DevOps practices and version control (Git, GitHub) * Strong problem-solving skills ...

DAS / ERRCS Designer

Ontario, CA · On-site

$35 - $45/hr

... modeling, heat mapping, link budget creation, and predictive coverage analysis using industry ... document control tasks such as printing, downloading, and filing. • Perform material and ...

DAS / ERRCS Designer

Ontario, CA · On-site

$35 - $45/hr

Conduct RF modeling, heat mapping, link budget creation, and predictive coverage analysis using ... Perform document control tasks such as printing, downloading, and filing. Perform material and ...

DAS / ERRCS Designer

Ontario, CA · On-site

$35 - $45/hr

... modeling, heat mapping, link budget creation, and predictive coverage analysis using industry ... document control tasks such as printing, downloading, and filing. • Perform material and ...

... models - using regression, predictive, and scenario analysis - to help anticipate and manage ... Analyze operational risk data, including loss distributions, RCSA drivers, loss events, control ...

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Model Predictive Control information

See Upland, CA salary details

$56K

$98.4K

$133.4K

How much do model predictive control jobs pay per year?

As of Sep 11, 2026, the average yearly pay for model predictive control in Upland, CA is $98,361.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,000.00 and $110,000.00 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 cities near Upland, CA are hiring for Model Predictive Control jobs?

Cities near Upland, CA with the most Model Predictive Control job openings:

Infographic showing various Model Predictive Control job openings in Upland, CA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 2% Contract, and 1% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $98,361 per year, or $47.3 per hour.

DATA SCIENTIST I-FINANCIAL & TIME SERIES FORECASTING with Security Clearance

Norco, CA • On-site

VSolvit
IT Services • 201 - 500 employees

Other

Medical, Dental, Vision, Life, Retirement

Re-posted yesterday


Job description

Job Summary We are seeking a talented and driven Data Scientist to join our evolving and dynamic team. This position is open to candidates at entry level of experience who possess a strong mathematical foundation and a passion for predictive modeling. In this role, you will build, validate, and maintain multi-variable time series forecasting models to project financial data and agency expenditures over multi-year horizons. You will work directly with financial records, taking ownership of the data lifecycle, from scraping, joining, and transforming raw datasets to rigorous model validation. We are looking for a resourceful problem-solver who excels at mathematical modeling, adapts quickly to changing data environments, and ensures our predictive tools remain accurate and resilient. As with any position, additional expectations exist. Some of these are, but are not limited to, adhering to normal working hours, meeting deadlines, following company policies as outlined by the Employee Handbook, communicating regularly with assigned supervisor(s), and staying focused on the assigned tasks including company meetings, and completing other tasks as assigned. Responsibilities * Time Series & Mathematical Modeling: Support the development and evaluation of statistical and time series forecasting models, including ARIMA, Prophet, regression, and tree-based models.
* Data Pipeline Construction & Scripting: Write and maintain Python scripts to scrape, extract, clean, join, and transform structured and unstructured financial data from web sources, APIs, databases, and raw files.
* Model Validation & Quality Assurance: Execute established validation workflows, compare model performance using metrics such as RMSE, MAE, and MAPE, and identify potential data-quality or data-leakage issues.
* Quantitative Feature Engineering: Assist with analyzing historical pricing, inflation indices, budget cycles, and spending patterns to create features for forecasting models.
* Data Analysis & Documentation: Perform exploratory data analysis and document data sources, assumptions, transformations, model results, and known limitations.
* Stakeholder Communication: Create reports, visualizations, and summaries that explain analytical findings to technical and non-technical stakeholders.
* Resourcefulness & Learning: Learn new domain requirements, proprietary databases, customer tools, and forecasting methods with guidance from senior team members.
* Compliance & Security: Maintain strict compliance with defense security protocols, confidentiality guidelines, and internal data handling policies. Basic Qualifications US Citizenship Required Ability to obtain and maintain a Secret Security Clearance * Bachelor's degree in Mathematics, Statistics, Computer Science, Data Science, Economics, Finance, Engineering, or a related quantitative field.
* Recent graduates and candidates with up to two years of relevant professional, internship, research, or academic experience are encouraged to apply.
* Foundational knowledge of linear algebra, calculus, probability, and statistics.
* Working knowledge of Python for data manipulation and analysis, including pandas and NumPy.
* Academic, internship, research, or project experience involving statistical modeling, machine learning, predictive analytics, or time series forecasting.
* Basic understanding of single-variable and multi-variable forecasting methods.
* Understanding of model evaluation concepts, including training and test datasets, error metrics, overfitting, and data leakage.
* Strong attention to detail and the ability to organize, document, and communicate analytical work.
* Strong verbal and written communication skills with the ability to explain technical concepts clearly.
* If applicable: If you are or have been recently employed by the U.S. government, a post-employment ethics letter will be required if employment is offered. Preferred Skills and Qualifications * Coursework, internship, research, or project experience in Economics, Finance, Econometrics, Accounting, or time series analysis.
* Experience with scikit-learn, statsmodels, Prophet, matplotlib, or similar analytical libraries.
* Familiarity with SQL, APIs, web scraping, ETL processes, or data-processing tools.
* Experience completing a capstone, thesis, internship, or personal project involving forecasting or predictive modeling.
* Exposure to government, defense, budgeting, or financial datasets.
* Familiarity with inflation adjustments, budget cycles, fiscal years, and macroeconomic factors.
* Familiarity with version control tools such as Git or GitHub.
* Continued education and interest in current and emerging AI/ML technologies.
* Strong problem-solving skills and the ability to work in a fast-paced environment. Company Summary Join the VSolvit Team! Founded in 2006, VSolvit (pronounced 'We Solve It') is a technology services provider that specializes in cybersecurity, cloud computing, geographic information systems (GIS), business intelligence (BI) systems, data warehousing, engineering services, and custom database and application development. VSolvit is an award winning WOSB, CA CDB, MBE, WBE, and CMMI Level 3 certified company. We offer a customizable health benefits program that best meets the needs of its employees. Offering may include: medical, dental, and vision insurance, life insurance, long and short-term disability and other insurance products, Health Savings Account, Flexible Spending Account, 401K Retirement Plan options, Tuition Reimbursement, and assorted voluntary benefits. Our goal is to grow together and enjoy the work that we do as a team. VSolvit LLC is an Equal Opportunity/Affirmative Action employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, national origin, protected veteran status, or disability status