ZipRecruiter

Log In

1

Convex Optimization Jobs (NOW HIRING)

QCi

Strong mathematical background in Convex Optimization, Quadratic Programming (QP), Mixed-Integer Linear Programming (MILP), or Gradient-Free Methods. * Experience with Numerical Analysis and high ...

SLB

AI Solutions Architect

Menlo Park, CA · On-site

$228K - $231K/yr

... and non-convex optimization techniques; (8) Time-series Analysis techniques with Statistics and AI; (9) Applied and mathematics statistics; (10) Cloud development tools and working in cloud ...

Cellanome

Machine Learning Engineer

Foster, OR · On-site +1

$160K - $215K/yr

Familiarity with optimization and estimation techniques such as convex optimization, Kalman filtering, Bayesian estimation, nonlinear optimization, or stochastic methods. We provide competitive total ...

Quantinuum

Computational Scientist - 705

Broomfield, CO · On-site

$80K - $101K/yr

Experience with numerical optimization techniques, such as convex optimization, genetic algorithms, or optimal control theory * Experience guiding multi-parameter optimization via exploratory but ...

Comity

Business Operations

Chicago, IL · On-site

$100K - $140K/yr

To accomplish our aims, we're leveraging state of the art statistical learning and convex optimization methods (AI) to build the financial rails of our future energy systems that will accelerate the ...

next page

Showing results 1-20

All JobsConvex Optimization Jobs

Convex Optimization information

See salary details

$16K

$55.8K

$102K

How much do convex optimization jobs pay per year?

As of Jun 16, 2026, the average yearly pay for convex 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 are the typical day-to-day tasks of someone working in a Convex Optimization role?

Professionals in Convex Optimization often spend their days formulating mathematical models, designing and implementing algorithms to solve optimization problems, and analyzing results to improve performance across various applications. They collaborate with data scientists, engineers, and domain experts to gather requirements and translate real-world challenges into solvable mathematical formulations. Additionally, they may be involved in code deployment, ensuring models are efficient and scalable for production use. Regular teamwork, troubleshooting, and staying current with the latest optimization research are also key parts of the job.

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

To thrive in a Convex Optimization role, you need a strong background in mathematics, particularly in optimization theory, linear algebra, and calculus, often supported by an advanced degree in mathematics, engineering, or computer science. Proficiency in programming languages such as Python, MATLAB, or Julia, and familiarity with optimization libraries and tools like CVX or Gurobi, are usually expected. Strong analytical thinking, problem-solving ability, and effective communication skills help you interpret complex problems and convey solutions to interdisciplinary teams. These skills are essential for designing robust optimization models that drive efficiency and innovation in fields like data science, finance, engineering, and operations research.

What is a Convex Optimization job?

A Convex Optimization job involves designing, analyzing, and implementing optimization algorithms to solve mathematical problems where the objective function and constraints are convex. Professionals in this field work in areas such as machine learning, finance, engineering, and operations research to improve efficiency and decision-making. They typically have expertise in linear and nonlinear programming, duality theory, and numerical algorithms. Jobs in this field require strong mathematical and programming skills, often using tools like Python, MATLAB, or CVX.

More about Convex Optimization jobs
What cities are hiring for Convex Optimization jobs? Cities with the most Convex Optimization job openings:
What are the most commonly searched types of Convex Optimization jobs? The most popular types of Convex Optimization jobs are:
What states have the most Convex Optimization jobs? States with the most job openings for Convex Optimization jobs include:
What job categories do people searching Convex Optimization jobs look for? The top searched job categories for Convex Optimization jobs are:
Convex Optimization jobs near you
Infographic showing various Convex Optimization job openings in the United States as of June 2026, with employment types broken down into 97% Full Time, and 3% Contract. Highlights an 78% Physical, 15% Hybrid, and 7% 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 12 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

Apply