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Mathematical Optimization Jobs (NOW HIRING)

Mytra

Senior Modeling & Optimization Engineer

Brisbane, CA · On-site

$125K - $172K/yr

... mathematical optimization, statistical modeling, or applied data science. • Ability to design experiments, analyze data, and communicate insights clearly to technical and non-technical audiences ...

QCi

We are looking for a Software Engineer with deep expertise in Mathematical Optimization and quantum algorithm development. This role is critical in architecting the core software engine that drives ...

Amazon

Research Scientist III - AMZ9443129

Seattle, WA · On-site

$159K - $215K/yr

Develop scalable mathematical models to derive optimal or near-optimal solutions to existing and new inventory planning challenges. Create prototypes and simulations to test devised solutions.

Brooksource

Lead DS- Remote

Miami, FL · On-site +1

$90 - $100/hr

Design and implement mathematical optimization frameworks for pricing strategies. * Partner with business stakeholders to solve complex revenue optimization problems. * Work with large-scale datasets ...

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How much do mathematical optimization jobs pay per year?

As of Jun 5, 2026, the average yearly pay for mathematical optimization in the United States is $55,794.00, according to ZipRecruiter salary data. Most workers in this role earn between $36,000.00 and $72,500.00 per year, depending on experience, location, and employer.

What is a Mathematical Optimization job?

A Mathematical Optimization job involves using mathematical techniques and algorithms to find the best possible solution to a given problem while satisfying constraints. Professionals in this field work in industries like finance, logistics, engineering, and artificial intelligence to optimize processes, minimize costs, or maximize efficiency. They use tools like linear programming, integer programming, and machine learning to solve complex decision-making problems.

What are some typical projects or problems tackled by professionals in Mathematical Optimization?

Professionals in Mathematical Optimization often work on projects involving resource allocation, supply chain management, scheduling, logistics, network design, or financial portfolio optimization. They use mathematical models to define and solve problems where the objective is to maximize efficiency or minimize costs under various constraints. Work may include collaborating with cross-functional teams to gather requirements, analyze large datasets, develop optimization algorithms, and implement solutions within existing business systems. These roles are found across industries such as manufacturing, transportation, finance, and technology, providing diverse and challenging opportunities. This variety in project scope allows for continuous learning and professional growth.

What are the key skills and qualifications needed to thrive in the Mathematical Optimization position, and why are they important?

To thrive in Mathematical Optimization, you need a strong background in mathematics, statistical modeling, and algorithm development, often supported by a degree in mathematics, operations research, engineering, or related fields. Proficiency with programming languages such as Python, MATLAB, or specialized optimization software (like Gurobi, CPLEX, or AMPL) is typically required. Strong analytical thinking, problem-solving skills, and the ability to communicate complex concepts clearly are critical soft skills for this role. These skills enable professionals to design effective solutions, interpret results, and convey recommendations to both technical and non-technical stakeholders.

More about Mathematical Optimization jobs
What cities are hiring for Mathematical Optimization jobs? Cities with the most Mathematical Optimization job openings:
What are the most commonly searched types of Mathematical Optimization jobs? The most popular types of Mathematical Optimization jobs are:
What states have the most Mathematical Optimization jobs? States with the most job openings for Mathematical Optimization jobs include:
What job categories do people searching Mathematical Optimization jobs look for? The top searched job categories for Mathematical Optimization jobs are:
Mathematical Optimization jobs near you
Infographic showing various Mathematical Optimization job openings in the United States as of May 2026, with employment types broken down into 62% Full Time, 35% Part Time, 2% Contract, and 1% Nights. Highlights an 94% Physical, and 6% Remote job distribution, with an average salary of $55,794 per year, or $26.8 per hour.
Senior Engineer, Quantum Algorithms

Senior Engineer, Quantum Algorithms

Quantum Computing Inc.

Hoboken, NJ • On-site

Full-time

Posted 2 days ago

Job description

Job Description Software Engineer, Quantum Algorithms (Optimization)
Location: Hoboken, NJ
Division: Technology
Department: Engineering
About Us Quantum Computing Inc. (QCi) (Nasdaq: QUBT) is an innovative, integrated photonics company that provides accessible and affordable quantum machines to the world today. QCi products are designed to operate at room temperature and low power at an affordable cost. The Company's portfolio of core technology and products offer unique capabilities in the areas of high-performance computing, artificial intelligence, cyber security as well as remote sensing applications.
Position Description: We are looking for a Software Engineer with deep expertise in Mathematical Optimization and quantum algorithm development. This role is critical in architecting the core software engine that drives our proprietary photonic quantum processors, combining complex mathematical formulations with physical optical hardware feedback.
Responsibilities
  • Design and implement the high-performance C++ runtime and Hardware Abstraction Layer (HAL) for photonic optimization computers.
  • Profile and optimize critical execution paths to minimize latency, addressing bottlenecks in memory bandwidth, cache locality, and data transfer.
  • Collaborate with FPGA, Electrical engineers and Firmware engineers to ensure to create, test, and optimize device interfaces.
  • Develop algorithmic enhancements to usage of quantum feedback to solve NP hard optimization problems more efficiently with higher solution quality.
  • Write efficient, thread-safe code for concurrent hardware control and real-time signal processing.
  • Design and implement novel algorithms that map optimization and machine-learning problems onto entropy-based photonic quantum processors, including post-processing pipelines.
  • Build software layers to decompose and orchestrate large-scale optimization problems across multiple photonic hardware resources.
  • Contribute to quantum algorithms on the company roadmap

Required Qualifications
  • 6+ years of experience in software engineering with a focus on systems or HPC.
  • Strong proficiency in C++ and Python
  • Experience with quantum algorithms, quantum information, or quantum optics.
  • Strong mathematical background in Convex Optimization, Quadratic Programming (QP), Mixed-Integer Linear Programming (MILP), or Gradient-Free Methods.
  • Experience with Numerical Analysis and high-performance math libraries (e.g., BLAS, LAPACK, Eigen).
  • Familiarity with protocols (e.g., UART, SPI, gRPC, REST) and software integration.
  • Strong understanding of performance tuning, memory management, and fault-tolerant design.
  • Familiarity with Linux system programming and build toolchains (CMake, GCC/Clang).
  • Experience working in cross-functional teams involving hardware, physics, and software.

Preferred Qualifications
  • Advanced degree (MS/PhD) in Computer Science, Physics, or Mathematics.
  • Experience with classical optimization solvers (e.g., CPLEX, Gurobi) or heuristic frameworks.
  • Familiarity with Open Quantum Systems or optical feedback mechanisms.
  • Background in Digital Signal Processing (DSP) or control theory.
  • Knowledge of containerized deployment using Docker.

Skills C++, Quantum Algorithms, High Performance Computing (HPC), Algorithm Design, Mathematical Optimization,, Multi-threading, Linux, CMake, Python, Performance Profiling, Hardware Abstraction, Signal Processing

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