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Mathematical Optimization Python Jobs (NOW HIRING)

Senior Optimization Engineer

San Francisco, CA · On-site

$123K - $169K/yr

This role requires deep expertise in mathematical optimization, strong software engineering skills in Python, and experience building optimization models that integrate with production systems. The ...

Senior Optimization Engineer

San Francisco, CA · On-site

$123K - $169K/yr

This role requires deep expertise in mathematical optimization, strong software engineering skills in Python, and experience building optimization models that integrate with production systems. The ...

Senior Optimization Engineer

San Francisco, CA · On-site

$123K - $169K/yr

This role requires deep expertise in mathematical optimization, strong software engineering skills in Python, and experience building optimization models that integrate with production systems. The ...

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Mathematical Optimization Python information

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How much do mathematical optimization python jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for mathematical optimization python in the United States is $58.62, according to ZipRecruiter salary data. Most workers in this role earn between $48.32 and $66.59 per hour, depending on experience, location, and employer.

What is mathematical optimization in Python?

Mathematical optimization in Python refers to the use of Python programming language and its libraries to find the best solution (minimum or maximum) for a mathematical problem, often under a set of constraints. This process involves defining an objective function and then using optimization algorithms to solve it. Python offers powerful libraries such as SciPy, PuLP, and CVXPY that make it easier to model and solve a wide range of optimization problems, including linear, nonlinear, and integer programming. Professionals in this field typically work on problems in logistics, finance, engineering, and data science, using these tools to make decisions that yield optimal outcomes.

What are the key skills and qualifications needed to thrive as a mathematical optimization Python specialist?

To thrive in Mathematical Optimization with Python, you need a solid background in mathematics, optimization theory, and proficiency in Python programming, often supported by a degree in mathematics, engineering, or computer science. Familiarity with optimization libraries like Pyomo, SciPy, or Gurobi, as well as experience with numerical computing tools, is typically required. Strong analytical thinking, problem-solving abilities, and clear communication skills help in translating business requirements into mathematical models and explaining solutions to non-technical stakeholders. These skills are critical for designing efficient optimization solutions that drive decision-making and operational efficiency in complex environments.

What are some common challenges faced when implementing mathematical optimization algorithms in Python, and how can they be addressed?

A frequent challenge in this role is ensuring algorithm efficiency and scalability, especially when dealing with large datasets or complex models. Python's ecosystem offers powerful libraries like Pyomo, SciPy, and Gurobi, but integrating them optimally requires both mathematical insight and software engineering skills. Debugging convergence issues, managing computational resources, and translating real-world problems into mathematical formulations are also common obstacles. Collaborating closely with data scientists, domain experts, and IT teams helps in refining models and deploying solutions effectively.

What is the difference between Mathematical Optimization Python vs Data Scientist?

AspectMathematical Optimization PythonData Scientist
Required SkillsPython, optimization algorithms, mathematical modelingPython, statistics, machine learning, data analysis
Work EnvironmentResearch, analytics, operations research teamsData analysis, predictive modeling, business insights
Industry UsageSupply chain, logistics, finance, manufacturingMarketing, finance, healthcare, tech

Mathematical Optimization Python focuses on developing models to optimize processes using Python and mathematical techniques. Data Scientists analyze data to extract insights and build predictive models. While both roles require Python skills, their core objectives and industry applications differ significantly.

Is mathematical optimization hard?

Mathematical optimization can be challenging due to its reliance on advanced mathematical concepts, algorithms, and problem-solving skills. For a role like a Mathematical Optimization Python developer, proficiency in programming, understanding of algorithms, and experience with optimization tools like linear programming or convex optimization are important. The difficulty often depends on the complexity of the problems and the level of expertise required.

What other helpful pages are available for Mathematical Optimization Python?

Other pages related to Mathematical Optimization Python:

Infographic showing various Mathematical Optimization Python job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 89% Full Time, 7% Part Time, and 3% Contract. Highlights an 79% Physical, 4% Hybrid, and 17% Remote job distribution, with an average salary of $121,932 per year, or $58.6 per hour.

Postdoctoral Researcher - Mathematical Optimization for Energy Systems

Golden, CO • On-site

$76K - $126K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 24 days ago


Key responsibilities

  • Collaborate with domain experts to identify suitable optimization approaches and stay informed about related research.

  • Adopt or develop mathematical, computing, and simulation frameworks to implement and evaluate optimization algorithms and solutions.

  • Identify opportunities to integrate AI/ reinforcement learning with classical optimization methods and develop implementations suitable for parallel computing architectures.


Job description

Posting TitlePostdoctoral Researcher - Mathematical Optimization for Energy Systems

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LocationCO - Golden

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Position TypePostdoc (Fixed Term)

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Hours Per Week40

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Working at NLRNLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.

Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.

At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.

Job Description

The Advanced Computing Solutions Group in the NLR Computational Science Center has an opening for a full-time Postdoctoral Researcher - Computational Sciences, with an emphasis on mathematical optimization and its application to the design and control of energy systems. We are looking for a dynamic researcher with a strong technical background to help us transform our energy future through advanced automation, control and decision making.

The successful candidate will have extensive experience with mathematical optimization formulations and algorithms and their application to physical systems. Additionally, the candidate will be familiar with parallel algorithmic approaches for large-scale linear, nonlinear, integer, and stochastic optimization problems. We anticipate that the research will involve integrating Artificial Intelligence (AI) techniques, such as reinforcement learning (RL), with classical mathematical optimization approaches and implementations. We seek candidates capable of pursuing research directions that combine these algorithmic components, using implementations that are suitable for effective utilization of the modern parallel computing architectures that are available at NRL. Candidates with creative problem-solving skills, interest in cross-disciplinary collaboration, and a passion for the mission and goals of both NLR and CMEI are of particular interest.

Responsibilities:

  • Collaborate with domain experts to identify where mathematical optimization constitutes a viable approach and maintain awareness of optimization-related research both at NLR and in the literature more generally.
  • Adopt existing - or develop new - mathematical, computing, and simulation frameworks required to implement and evaluate the performance of optimization algorithms and solutions.
  • Creatively identify new opportunities to leverage AI/RL to augment or enhance classical optimization algorithms and/or formulations.
  • Author publications and contribute to proposals to sustain research directions.

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Basic QualificationsMust be a recent PhD graduate within the last three years.

* Must meet educational requirements prior to employment start date.

Additional Required Qualifications
  • Experience formulating optimization problems in an algebraic modeling language, e.g., Pyomo, JuMP, PuLP, GAMS.
  • Experience with mathematical optimization solvers, e.g., CPLEX, Gurobi, Xpress, Cbc, Ipopt, and their capabilities.
  • Good understanding of optimization fundamentals, both computational and mathematical.
Preferred Qualifications
  • Familiarity with distributed computing frameworks such as MPI and OpenMP
  • Experience with Pyomo and/or JuMP
  • Experience programming in Python and/or Julia
  • Experience with scalable machine learning frameworks, e.g, PyTorch
  • Experience working with diverse, inclusive, and cross-disciplinary research teams

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Job Application Submission Window

The anticipated closing window for application submission is up to 30 days and may be extended as needed.

Annual Salary Range (based on full-time 40 hours per week)Job Profile: Postdoctoral Researcher / Annual Salary Range: $76,600 - $126,400

NLR takes into consideration a candidate's education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee's salary history will not be used in compensation decisions.

Benefits SummaryBenefits include medical, dental, and vision insurance; short-term disability insurance*; pension benefits*; 403(b) Employee Savings Plan with employer match*; life and accidental death and dismemberment (AD&D) insurance; personal time off (PTO) and sick leave; and paid holidays. NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement.

* Based on eligibility rules

Badging RequirementNLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation.Drug Free Workplace

NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.

If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.

Submission Guidelines

Please note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.

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Equal Opportunity Employer

All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.

Reasonable Accommodations

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E-Verify is a registered trademark of the U.S. Department of Homeland Security. This business uses E-Verify in its hiring practices to achieve a lawful workforce.