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Convex Optimization Jobs (NOW HIRING)

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 ...

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

Experience in convex optimization, computational geometry or linear algebra. * Experience in GPU/CUDA/TensorRT * Previous internships involving large-scale deep learning models and systems

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

Experience in convex optimization, computational geometry or linear algebra. * Experience in GPU/CUDA/TensorRT * Previous internships involving large-scale deep learning models and systems

pony.ai

Research Intern - Deep Learning

Fremont, CA · On-site

$7.0K - $10K/mo

Experience in convex optimization, computational geometry or linear algebra. * Experience in GPU/CUDA/TensorRT * Previous internships involving large-scale deep learning models and systems

SpaceX

Software Engineer, CDN (Starlink)

Palo Alto, CA

$155K - $185K/yr

Knowledgeable in network topology, numerical optimization techniques, graph theory approaches, or convex optimization. * Developed, debugged, and deployed scalable software that has been used in real ...

New

Cnpc Usa Corporation

AI Solutions Architect

Houston, TX · On-site

Computational intelligence and non-convex optimization techniques. * Time-series analysis techniques using statistics and AI. * Applied mathematics and statistics. * Cloud development tools and cloud ...

SpaceX

Software Engineer (Starlink Mobile)

Sunnyvale, CA

$155K - $185K/yr

Knowledgeable in network topology, numerical optimization techniques, graph theoretic approaches, or convex optimization * Developed, debugged, and deployed software that has been used in real world ...

Comity

Originations Lead

New York, NY · On-site

$175K - $210K/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 ...

Cellanome

Machine Learning Engineer

San Diego, CA · On-site

$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 ...

SpaceX

Software Engineer (Starlink Mobile)

Redmond, WA

$145K - $175K/yr

Knowledgeable in network topology, numerical optimization techniques, graph theoretic approaches, or convex optimization * Developed, debugged, and deployed software that has been used in real world ...

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Convex Optimization information

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$16K

$55.8K

$102K

How much do convex optimization jobs pay per year?

As of Jul 10, 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 July 2026, with employment types broken down into 40% Internship, 8% As Needed, 13% Full Time, 28% Temporary, and 11% Nights. Highlights an 80% Physical, 10% Hybrid, and 10% Remote job distribution, with an average salary of $55,794 per year, or $26.8 per hour.

Quantitative Researcher for Congestion Revenue Rights

Comity

New York, NY • On-site, Remote

$160K - $200K/yr

Full-time

Re-posted 13 days ago

Job description

Why we exist
We're on a mission to improve the reliability, transparency, and efficiency of our energy systems, fostering a future with sustainable and abundant energy. 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 deployment of clean energy resources.
We envision energy systems that are efficient, autonomous, resilient, and powered by 100% renewable energy.
Who we are
Our founders (ex-Apple, Bluevine; ex-Affirm, Square, Google) are Stanford alumni with experience in complex systems, machine learning and structured finance. Our world-class investors, Maverick Ventures and Caffeinated Capital, are aligned to our policy objectives and platform vision.
We have hubs in Chicago, New York City, and San Francisco.
The Role
Comity is looking for a Quantitative Researcher for Congestion Revenue Rights to help us design, deploy, and operate autonomous, systematic strategies that realize economic value from congestion auctions, using our future information (forecasts) and energy systems models, under real-world market constraints. You'll
  • Build a market-leading CRR book leveraging our proprietary technologies
  • Architect autonomous strategies end to end - model, forecasts, market actions
  • Work end-to-end from information development to production-ready code if resources are otherwise occupied

Comity has two core teams: Power Markets and Commercial. This role is part of our Power Markets team.
We're excited about you because:
  • You have 5+ years of experience applying quantitative methods to FTRs, PTP/UTC products, and/or congestion forecasting in ERCOT.
  • You have applied stochastic optimization to problems in financial or electrical engineering, operations research, or economics.
  • You have conceptual fluency with probability theory and good taste for shaping and managing distributions.
  • You have an advanced degree in quantitative finance, computer science, statistics, machine learning, operations research, or a related quantitative field.
  • You are experienced and comfortable developing and monitoring machine learning models.
  • You are a skilled programmer in Python.
    More importantly -
    • You are extraordinarily driven - you're relentless in getting research artifacts into production and willing and capable to work end-to-end (forecasts, models, optimization, production-quality code) when supporting resources are contended.
    • You love winning - while you prioritize well, there's no task too tedious if it drives performance.
    • You are predisposed to collaboration - we maintain a highly open and discursive environment which rewards the refinement of everyone's ideas and requires genuine curiosity.
Location
We have hubs in Chicago, New York City, and San Francisco.
At Comity, we seek to recruit, develop, and retain the most talented people from a diverse candidate pool. Our priority is to ensure that all applicants are provided with fair and equal access to employment opportunities. Recruiting and hiring decisions are made without regard to race, color, religion, sex, national origin, age, disability, or any other class protected by law.

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