1

Combinatorial Optimization Jobs (NOW HIRING)

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.

Staff AI Scientist

Mountain View, CA · On-site

$205.50K - $278K/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.

Staff AI Scientist

New York, NY

$209.50K - $283.50K/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.

Staff AI Scientist

San Diego, CA · On-site

$209.50K - $283.50K/yr

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

Manhattan, NY · On-site

$201.50K - $273K/yr

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

$209.50K - $283.50K/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.

Staff AI Scientist

Oakland, CA

$209.50K - $283.50K/yr

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

Manhattan, NY · On-site

$209.50K - $283.50K/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.

Staff AI Scientist

Mountain View, CA · On-site

$205.50K - $278K/yr

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

next page

Showing results 1-20

Combinatorial Optimization information

See salary details

$41.5K

$142.5K

$201K

How much do combinatorial optimization jobs pay per year?

As of Jun 1, 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 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.

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.

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:
Infographic showing various Combinatorial Optimization job openings in the United States as of May 2026, with employment types broken down into 92% Full Time, and 8% Part Time. Highlights an 20% Physical, 1% Hybrid, and 79% Remote job distribution, with an average salary of $142,460 per year, or $68.5 per hour.
Senior Staff AI Scientist

Senior Staff AI Scientist

Intuit

Mountain View, CA

Full-time

Posted 6 days ago


Intuit rating

8.4

Company rating: 8.4 out of 10

Based on 81 frontline employees who took The Breakroom Quiz

64th of 184 rated software companies


Job description

Intuit is looking for innovative and hands-on Senior Staff AI Scientist to join the Intuit AI team.

Come join our collaborative and creative group of AI scientists and machine learning engineers and build models that directly affect hundreds of thousands of our customers. In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches. 


Responsibilities

  • Practices leadership and communication skills to influence teams and to evangelize AI science across the organization

  • Collaborates with stakeholders to define success criteria and align model metrics with business goals. Works side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products

  • Leads technical work of a scrum team: initiating and designing model solutions, driving end-to-end architecture designs of the team's work, and holding the team accountable for high quality code, git, design, costs and implementation standards

  • Performs hands-on data analysis and modeling with large data sets, including discovering data sources, getting data access, cleaning up data, and making them "model-ready". You need to be willing and able to do your own ETL and design/build featurization. 

  • Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.  

  • Runs A/B tests to draw conclusions on the impact of your team's work and communicates results to peers and leaders

  • Communicates with partners to ensure successful delivery and integration of DS solutions.  

  • Proactively researches, explores, and enables new ML technologies. Keeps up with the new developments in academia and industry and considers possible extensions to solve Intuit customer problems. 


Qualifications

  • 6+ years of industry experience with AI science

  • BS, MS or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent

  • 4+ years of hands-on expertise in ML paradigms such as Causal-ML, supervised/unsupervised, Online, Bayesian, Reinforcement or Deep Learning.

  • Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization.

  • Proficient  in NLP techniques, Explainable AI, and ML frameworks. 

  • Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R.

  • Efficient in SQL, Hive, SparkSQL, etc.

  • Comfortable working in a Linux environment

  • Experience with building end-to-end reusable pipelines from data acquisition to model output delivery

  • Quick learner, adaptable, with the ability to work independently in a fast-paced environment

  • Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users


Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: 

Bay Area California $ 222,000- 300,000


Employment Type: Full-Time

What Intuit employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom