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Remote Convex Optimization Jobs (NOW HIRING)

Federato Technologies

Data Scientist

San Francisco, CA · On-site +1

$160K - $200K/yr

This is a remote position, but we do have an office in San Fransisco. You will be the first data ... Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization ...

Federato Technologies

Data Scientist

San Francisco, CA · Remote

$160K - $200K/yr

This is a remote position, but we do have an office in San Fransisco. You will be the first data ... Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization ...

Cellanome

Machine Learning Engineer

Foster, OR · On-site +1

$160K - $215K/yr

Possibility for Remote. Key Responsibilities: * Design, develop, and optimize advanced algorithms ... Familiarity with optimization and estimation techniques such as convex optimization, Kalman ...

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Remote Convex Optimization information

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$16K

$55.8K

$102K

How much do remote convex optimization jobs pay per year?

As of Jun 6, 2026, the average yearly pay for remote 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.

How do remote convex optimization professionals typically collaborate with cross-functional teams to ensure project success?

Remote convex optimization professionals often work closely with data scientists, software engineers, and business stakeholders to define project goals and translate optimization problems into actionable solutions. Effective communication is key, as much of the collaboration occurs through virtual meetings, shared documentation, and project management tools. Regular updates, code reviews, and collaborative troubleshooting help ensure that optimization models align with business needs and technical requirements. By fostering an open and responsive communication style, remote professionals can overcome the challenges of distance and maintain productive teamwork.

What is the difference between Remote Convex Optimization vs Remote Data Scientist?

AspectRemote Convex OptimizationRemote Data Scientist
Required CredentialsMathematics, Optimization, Computer Science degreesStatistics, Mathematics, Computer Science degrees
Work EnvironmentResearch, algorithm development, mathematical modelingData analysis, modeling, machine learning projects
Industry UsageFinance, tech, operations researchTech, healthcare, finance, marketing
Common Search/ComparisonFocus on optimization techniques and algorithmsFocus on data analysis and predictive modeling

Remote Convex Optimization specialists focus on developing algorithms to solve optimization problems, often requiring strong mathematical and programming skills. Remote Data Scientists analyze data to extract insights, using statistical and machine learning techniques. While both roles involve data and programming, their core focuses differ: optimization versus data analysis.

What is a remote convex optimization job?

A remote convex optimization job involves working on mathematical problems and algorithms that focus on optimizing convex functions—where any local minimum is also a global minimum—while collaborating with teams or clients from a remote location. Professionals in this role develop, analyze, and implement optimization models for applications such as machine learning, finance, logistics, or engineering. The remote aspect allows for flexible work arrangements, enabling experts to contribute from anywhere with an internet connection. Core skills include mathematics, programming (often in Python or MATLAB), and familiarity with optimization libraries and tools.

What are the key skills and qualifications needed to thrive as a Remote Convex Optimization Specialist, and why are they important?

To thrive as a Remote Convex Optimization Specialist, you need a strong background in mathematics, optimization theory, and experience with algorithm development, typically supported by an advanced degree in applied mathematics, computer science, or engineering. Proficiency with programming languages such as Python or MATLAB, and familiarity with optimization libraries like CVXPY or Gurobi, are commonly required. Analytical thinking, problem-solving, and clear communication are crucial soft skills for collaborating with distributed teams and interpreting complex results. These skills ensure accurate modeling, efficient problem-solving, and effective remote collaboration in developing optimization solutions.
More about Remote Convex Optimization jobs
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Remote Convex Optimization jobs near you
Data Scientist

Data Scientist

Federato Technologies

San Francisco, CA • On-site, Remote

$160K - $200K/yr

Full-time

Posted 4 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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