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Convex Optimization Jobs in California (NOW HIRING)

Federato Technologies

Data Scientist

San Francisco, CA · Remote

$160K - $200K/yr

Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization), either PhD or Advanced MS degree. * Comfortable with Python, Flask/Django, Pandas and Numpy

Convex

Software Engineer, AI

San Francisco, CA · On-site

The projects involve range from small ergonomic tweaks to make agentic development seamless, large structural changes like branching and automatic performance optimization of Convex projects, and ...

Federato Technologies

Data Scientist

San Francisco, CA · On-site +1

$160K - $200K/yr

Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization), either PhD or Advanced MS degree. * Comfortable with Python, Flask/Django, Pandas and Numpy

Inversion

Staff GNC Engineer (Guidance)

Vista, CA · On-site

$161K - $221K/yr

Experience with modern convex optimization methods for guidance (e.g., successive convexification / sequential convex programming, lossless convexification) * Experience developing and exercising 3 ...

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Convex Optimization information

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$15.8K

$55.1K

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How much do convex optimization jobs pay per year?

As of Aug 15, 2026, the average yearly pay for convex optimization in California is $55,064.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,500.00 and $71,600.00 per year, depending on experience, location, and employer.

What does someone working in convex optimization do?

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 convex optimization?

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 convex optimization?

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.

What are the most commonly searched types of Convex Optimization jobs in California?

The most popular types of Convex Optimization jobs in California are:

What are popular job titles related to Convex Optimization jobs in California?

For Convex Optimization jobs in California, the most frequently searched job titles are:

What job categories do people searching Convex Optimization jobs in California look for?

The top searched job categories for Convex Optimization jobs in California are:

What cities in California are hiring for Convex Optimization jobs?

Cities in California with the most Convex Optimization job openings:

Convex Optimization jobs near you
Infographic showing various Convex Optimization job openings in California as of August 2026, with employment types broken down into 1% Internship, 84% Full Time, 9% Part Time, 2% Temporary, and 4% Contract. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution, with an average salary of $55,064 per year, or $26.5 per hour.

Data Scientist

Federato Technologies

San Francisco, CA • Remote

$160K - $200K/yr

Full-time

Re-posted 13 days ago

Job description

Company Description

Federato Technologies is a Series A startup based in San Fransisco, CA looking to hire a data scientist. Federato is a venture backed company funded by some of the most prominent VC's in the Bay Area. We build software to help large insurance carriers improve their portfolio optimization. Our goal is to close down the $150B insurance gap by making insurance affordable again. The initial algorithms used in our product spun out off the founders published research in Reinforcement Learning, Statistics, and Optimization.

The role will report directly to the CTO. Next to the CTO, you will be first Data Scientist on the team building out models and algorithms pivotal to our value proposition. 

This is a remote position, but we do have an office in San Fransisco.

Job Description

You will be the first data scientist on the team working through and building models from scratch that will be pivotal for our business. You will have a huge impact on our success and will be part of a fast growing team!

Qualifications

  • Demonstrated interest in engineering for impact

  • 2+ years of hands-on industry experience with machine learning and optimization models

  • 2+ years of experience with model deployment, monitoring and version control

  • Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization), either PhD or Advanced MS degree.

  • Comfortable with Python, Flask/Django, Pandas and Numpy

  • Demonstrated ability to pick up new technologies quickly and learn on the spot

Additional Information

Pay Range: $160k - $200k

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