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Mathematical Optimization Python Jobs (NOW HIRING)

Gurobi Optimization

Senior MIP Developer (Global Remote)

$55.75 - $73.75/hr

A mission that focuses on mathematical optimization. We empower our customers to expand their use ... C#, Python, Matlab, and R, is considered a plus. Your Alignment with our Gurobi Core Values:

KAPI LLC

Senior MLOps Engineer - Snowflake

Dallas, TX · On-site

$100K - $130K/yr

Performance optimization * Python integration * Snowflake integration with ML pipelines Experience ... AI, Mathematical Optimization, and Cloud Platform Engineering. We build highly personalized ...

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How much do mathematical optimization python jobs pay per hour?

As of Sep 10, 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.

Senior Engineer, Quantum Algorithms (Optimization)

Hoboken, NJ • On-site

QUANTUM COMPUTING, INC.
Computer and Peripheral Equipment Manufacturing • 51 - 200 employees
Learn more about QUANTUM COMPUTING, INC.external

$143K - $201K/yr

Full-time

Re-posted 19 days ago

Key responsibilities

  • Design and implement the high-performance C++ runtime and Hardware Abstraction Layer for photonic optimization computers.

  • Develop algorithmic enhancements to use quantum feedback for solving NP-hard optimization problems more efficiently.

  • Build software layers to decompose and orchestrate large-scale optimization problems across multiple photonic hardware resources.

Job description

Job Title: 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

Quantum Computing Inc. (QCi) is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.

Incumbent(s) in this position may be required to perform other duties and special assignments not specifically stated above. Statements outlined in this section are designated as essential job functions in accordance with the Americans with Disabilities Act of 1990.