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Combinatorial Optimization Jobs (NOW HIRING)

Strong foundation in mathematics and theoretical computer science, such as linear algebra, calculus, graph theory, computational geometry, combinatorial optimization algorithms, stochastic processes ...

Strong foundation in mathematics and theoretical computer science, such as linear algebra, calculus, graph theory, computational geometry, combinatorial optimization algorithms, stochastic processes ...

Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

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Combinatorial Optimization information

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$41.5K

$142.5K

$201K

How much do combinatorial optimization jobs pay per year?

As of Jul 18, 2026, the average yearly pay for combinatorial optimization in the United States is $142,460.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,500.00 and $166,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Combinatorial Optimization Specialist, and why are they important?

To thrive as a Combinatorial Optimization Specialist, you need a solid background in mathematics, computer science, and operations research, often supported by an advanced degree in a related field. Familiarity with programming languages (such as Python, C++, or Java), optimization libraries, and mathematical modeling tools like CPLEX or Gurobi is typically required. Strong analytical thinking, problem-solving skills, and effective communication help you devise and explain complex solutions to stakeholders. These skills are crucial for developing efficient algorithms and models that address challenging optimization problems in various industries.

How does a Combinatorial Optimization specialist typically collaborate with other departments within an organization?

Combinatorial Optimization specialists frequently work cross-functionally, partnering with data scientists, software engineers, and business analysts to translate complex business problems into mathematical models. They help teams identify optimal solutions for scheduling, routing, resource allocation, and other operational challenges. Effective communication is crucial, as specialists must explain complex algorithms to non-technical stakeholders and integrate their solutions into broader business processes. Collaborative teamwork and iterative problem-solving are common in this role.

What is the difference between Combinatorial Optimization vs Data Analyst?

AspectCombinatorial OptimizationData Analyst
Required CredentialsMathematics, Operations Research, Computer Science degreesStatistics, Data Science, Business Analytics degrees
Work EnvironmentResearch labs, consulting firms, tech companiesCorporate offices, finance, marketing departments
Industry UsageLogistics, manufacturing, AI, supply chainFinance, marketing, healthcare, retail

While both roles involve analytical skills, Combinatorial Optimization focuses on solving complex mathematical problems to find optimal solutions, often in logistics and operations. Data Analysts interpret data to inform business decisions, working across various industries. Understanding these differences helps clarify career paths and employer expectations.

What is combinatorial optimization?

Combinatorial optimization is a field in mathematics and computer science focused on finding the best solution from a finite set of possible solutions. It involves problems where you need to arrange, select, or group discrete objects according to certain rules to achieve an optimal outcome. Examples include scheduling, routing, and assignment problems. Techniques such as linear programming, branch and bound, and heuristics are often used to solve these problems. Combinatorial optimization is widely applied in logistics, operations research, computer science, and engineering.
More about Combinatorial Optimization jobs
What cities are hiring for Combinatorial Optimization jobs? Cities with the most Combinatorial Optimization job openings:
What states have the most Combinatorial Optimization jobs? States with the most job openings for Combinatorial Optimization jobs include:
What job categories do people searching Combinatorial Optimization jobs look for? The top searched job categories for Combinatorial Optimization jobs are:
Infographic showing various Combinatorial Optimization job openings in the United States as of July 2026, with employment types broken down into 3% As Needed, 2% Full Time, 4% Part Time, 9% Temporary, 78% Contract, and 4% Nights. Highlights an 24% Physical, 1% Hybrid, and 75% Remote job distribution, with an average salary of $142,460 per year, or $68.5 per hour.
Post-Doctoral Fellow in Operations Research

Post-Doctoral Fellow in Operations Research

Carnegie Mellon University

Pittsburgh, PA • On-site

$47K - $64K/yr

Full-time

Posted 10 days ago


Carnegie Mellon University rating

8.6

Company rating: 8.6 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

56th of 555 rated colleges and universities


Job description

Description
Postdoctoral Position at Carnegie Mellon University
The Heinz College of Information Systems and Public Policy at Carnegie Mellon University is now accepting applications for postdoctoral research fellow positions in operations research and analytics. The positions will investigate the role of data, information, and uncertainty in decision making, with a particular emphasis on robust optimization and the design and operation of resilient, and robust systems for distributed health delivery.
Research at Heinz College (https://www.heinz.cmu.edu/) is driven by a strong passion to understand and improve our society analytically - in areas such as healthcare management, rural health, distributed health delivery, mobility, statistical fairness, privacy, supply chains, organizational behavior, workforce economics, and the future of work. Successful candidates will join an active group of colleagues with expertise spanning operations research, optimization, statistics, information systems, economics, and public policy.
The core research agenda centers on robust optimization theory and its applications to real-world decision-making under uncertainty. Possible topics for the postdoctoral positions include classical robust optimization; distributionally robust optimization and data-driven uncertainty or ambiguity sets; resilient supply chains; rural and distributed health delivery systems; healthcare operations and access; improving mobility; identifying and improving social determinants of health; automation in logistics; AI safety; and understanding the theoretical properties of decision models that naturally arise from these application domains.
The postdoctoral fellows will report primarily to Prof. Peter Zhang and Prof. Holly Wiberg. They will be encouraged to publish in top-tier journals and conferences and may collaborate both within Heinz and across Carnegie Mellon University, including the Tepper School of Business, the Department of Civil and Environmental Engineering, the School of Computer Science, and CMU's broader entrepreneurship ecosystem. Fellows will receive regular mentorship throughout the lifecycle of research projects and will be encouraged to disseminate research findings in major conferences and university seminars. Opportunities may also be available to mentor graduate students and to explore commercialization pathways for research with practical impact.
The appointment is for one year, with an option of renewal for a second year contingent on satisfactory progress. The preferred start date is flexible, with earliest availability in Fall 2026.
Qualifications
Candidates should have a PhD by Fall 2026 in operations research, industrial engineering, management science, computer science, mathematics, statistics, or a closely related field. Successful candidates should have a strong research record in optimization, broadly defined, with solid mathematical foundations in areas such as linear programming, convex optimization, combinatorial optimization, stochastic optimization, or robust optimization. Experience with distributionally robust optimization, machine learning, data-driven methods, healthcare delivery, supply chains, or other applied decision-making problems is especially welcome. Excellent written and oral communication skills are expected.
Application Instructions
Interested candidates should submit the following materials:
• Curriculum vitae, including full publication list;
• Research statement, up to 2 pages, describing past work and future research interests;
• One to two representative publications or preprints;
• Three references.
Applications will be reviewed on a rolling basis and will remain open until the positions are filled

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