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Mathematical Engineer Jobs in California (NOW HIRING)

ACI Infotech

Senior Java Developer

Newport Beach, CA · On-site

$61.25 - $78.25/hr

Computer Science/ Math / Engineering background * 10+ Year of hands on working experience as software engineer/developer. * Fluent in Java 8 + Spring. * Experience in GitLab CI/CD. * Experience in ...

ACI Infotech

Sr Java Developer

Newport Beach, CA · On-site

$61.25 - $78.25/hr

Computer Science/ Math / Engineering background * 10+ Year of hands on working experience as software engineer/developer. * Fluent in Java 8 + Spring. * Experience in GitLab CI/CD. * Experience in ...

TecTammina

Senior Software Engineer

Irvine, CA · On-site

$131K - $173K/yr

Mathematics, Engineering). Qualifications Event driven UI experience in Swing, Flex, GWT, MooTools, or equivalent. Experience consuming RESTful web services with JSON and XML. Experience with JQuery ...

Mavrx

Algorithm Test Engineer

San Francisco, CA · On-site

Qualifications Education: * BS or greater in math, engineering, physics, statistics, etc. Required Experience: * Proficient in at least one programming language, ideally a statistical analysis ...

Mavrx

Algorithm Test Engineer

San Francisco, CA · On-site

Qualifications Education: * BS or greater in math, engineering, physics, statistics, etc. Required Experience: * Proficient in at least one programming language, ideally a statistical analysis ...

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All JobsMathematical Engineer JobsMathematical Engineer Jobs in California

Mathematical Engineer information

See California salary details

$36K

$105.9K

$135.7K

How much do mathematical engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for mathematical engineer in California is $105,877.00, according to ZipRecruiter salary data. Most workers in this role earn between $87,300.00 and $134,200.00 per year, depending on experience, location, and employer.

What is a mathematical engineer?

A Mathematical Engineer applies advanced mathematical concepts, computational techniques, and algorithms to solve complex engineering and technical problems. They work in various industries such as finance, data science, cryptography, and simulation modeling. Their role involves designing mathematical models, optimizing systems, and analyzing large datasets to improve efficiency and decision-making. They often collaborate with engineers, scientists, and analysts to develop innovative solutions for real-world challenges.

What are the key skills and qualifications needed to thrive as a mathematical engineer?

To thrive as a Mathematical Engineer, you need a strong background in advanced mathematics, mathematical modeling, and engineering principles, usually supported by a degree in mathematics, engineering, or a related field. Proficiency with programming languages such as MATLAB, Python, or R, and familiarity with simulation or modeling software is often expected. Exceptional problem-solving abilities, effective communication, and teamwork skills are highly valued in this interdisciplinary role. These competencies enable Mathematical Engineers to develop robust solutions for complex engineering challenges and collaborate successfully with diverse technical teams.

What are common challenges faced by mathematical engineers in their daily work?

Mathematical Engineers often tackle challenges such as translating real-world engineering problems into precise mathematical models and ensuring those models accurately represent system behaviors. Working with large datasets, complex algorithms, or advanced simulations requires strong analytical skills and attention to detail. Close collaboration with other engineers, researchers, and technical experts is common, so balancing technical depth with clear communication can be demanding but rewarding. Overcoming these challenges helps Mathematical Engineers deliver impactful, data-driven solutions in industries like aerospace, finance, or manufacturing.

What does a mathematical engineer do?

A mathematical engineer applies advanced mathematical techniques and models to solve complex problems in engineering, science, and technology. They often work with data analysis, simulations, and algorithm development using tools like MATLAB or Python, and may be involved in research, product development, or optimization tasks.

What are the most commonly searched types of Mathematical Engineer jobs in California?

The most popular types of Mathematical Engineer jobs in California are:

What are popular job titles related to Mathematical Engineer jobs in California?

For Mathematical Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Mathematical Engineer jobs in California look for?

The top searched job categories for Mathematical Engineer jobs in California are:

What cities in California are hiring for Mathematical Engineer jobs?

Cities in California with the most Mathematical Engineer job openings:

Mathematical Engineer jobs near you
Infographic showing various Mathematical Engineer job openings in California as of August 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $105,877 per year, or $50.9 per hour.

Staff Software Engineer, Operations Research

Wing

Palo Alto, CA • On-site

$241K/yr

Full-time

Re-posted 3 days ago

Job description

About the Role:

Wing is looking for a Staff Software Engineer, Operations Research to join our Delivery Network team. This role is hybrid based in Palo Alto, CA.

As a Staff Software Engineer, Operations Research, you will own the mathematical foundation of our delivery network. Moving physical goods through the sky autonomously introduces dynamic constraints: battery conditions, real-time weather patterns, airspace de-confliction, and shifting marketplace demand.

You will dive into a wealth of flight and logistics data, using advanced optimization techniques to maximize value for our consumers, our partners, and our fleet. You will exercise independent judgment to define our technical roadmap and bridge the gap between abstract mathematical models and our production environment. You will leverage advanced solvers and simulations to build solutions that scale with Wing's business.

What You'll Do: 

  • Design and Implement Algorithms: Build new algorithm components within our production delivery network system to meet emerging requirements. We work with Google OR-Tools and the researchers that develop it.
  • Extract Data-Driven Insights: Analyze complex logistics data to identify network inefficiencies, translating those insights directly into production-ready algorithm enhancements.
  • Integrate OR and ML: Combine state-of-the-art optimization and machine learning techniques (e.g., using ML for demand forecasting and Tools for fleet positioning) to improve the speed and quality of our dispatching decisions.
  • Lead Cross-Functionally: Collaborate tightly with software engineers, data scientists, hardware teams, and product managers to develop scalable, cross-cutting solutions that balance physical constraints with business value.
  • Exercise Strategic Autonomy: Evaluate technical options, make informed architectural decisions, and determine the appropriate OR methodologies to solve ambiguous, open-ended problems.
  • Mentorship: Elevate the technical rigor of the team by guiding junior scientists and engineers in OR fundamentals, code quality, and algorithm design.

What You'll Need: 

  • 12+ years of industry or post-graduate experience solving complex optimization problems using mathematical programming or metaheuristics.
  • Ph.D. or Master's degree in Operations Research, Industrial Engineering, Computer Science, Applied Mathematics, or a closely related field.
  • Strong coding ability in Python, C++, or Java. You must understand object-oriented programming, functional programming concepts, and core computer science algorithms.
  • A proven track record of designing algorithms that don't just live in research papers, but operate efficiently in real-time production software systems.
  • Previous experience in aviation, autonomous vehicles, ride-sharing, or last-mile logistics.
  • Deep familiarity with other commercial or open-source optimization solvers (e.g., Gurobi, CPLEX, SCIP).
  • Experience with cloud computing platforms (GCP, AWS) and integrating solvers into scalable, distributed systems.
  • Experience modeling systems for simulation and designing experiments. We use a combination of discrete event simulations and high fidelity physics simulations.

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