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Mathematical Optimization Engineer Jobs (NOW HIRING)

This role requires deep expertise in mathematical optimization, strong software engineering skills in Python, and experience building optimization models that integrate with production systems. The ...

Senior Optimization Engineer

San Francisco, CA · On-site

$123K - $169K/yr

This role requires deep expertise in mathematical optimization, strong software engineering skills in Python, and experience building optimization models that integrate with production systems. The ...

Senior Optimization Engineer

San Francisco, CA · On-site

$123K - $169K/yr

This role requires deep expertise in mathematical optimization, strong software engineering skills in Python, and experience building optimization models that integrate with production systems. The ...

Optimization Engineer

Oklahoma City, OK · On-site

$80 - $100/hr

What we're looking for * 4+ years of optimization, operations research, industrial engineering, or ... Strong Python and mathematical modeling skills. * Experience with linear, mixed‑integer ...

As a Senior Forecasting and Optimization Engineer you will work closely with some of the brightest ... Strong mathematical skills, with the ability to apply new mathematical techniques to real-world ...

As a Senior Forecasting and Optimization Engineer you will work closely with some of the brightest ... Strong mathematical skills, with the ability to apply new mathematical techniques to real-world ...

Optimization Engineer

$217K - $237K/yr

The Optimization Engineer will provide technical leadership in the creation of a programmatic ... mathematical foundation. * Strong knowledge of energy systems, wholesale power markets, or power ...

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Mathematical Optimization Engineer information

What does a mathematical optimization engineer do?

A Mathematical Optimization Engineer designs and implements algorithms to solve complex optimization problems in areas like logistics, finance, manufacturing, or data science. They use mathematical models and computational techniques to find the most efficient solutions to real-world challenges, such as minimizing costs or maximizing efficiency. Their work often involves collaborating with other engineers and data scientists, coding optimization routines, and translating business requirements into mathematical formulations. This role typically requires a strong background in mathematics, programming, and problem-solving.

How does a mathematical optimization engineer typically collaborate with cross-functional teams during a project?

Mathematical Optimization Engineers often work closely with data scientists, software developers, and business analysts to translate complex business problems into mathematical models. They contribute their expertise by designing, implementing, and refining optimization algorithms that fit project requirements. Regular communication is essential, as they must clearly explain technical concepts to non-experts and adapt models based on stakeholder feedback. This collaborative environment helps ensure that solutions are both technically sound and aligned with organizational goals.

What are the key skills and qualifications needed to thrive as a mathematical optimization engineer, and why are they important?

To thrive as a Mathematical Optimization Engineer, you need a strong background in mathematics, operations research, and computer science, typically supported by a relevant degree such as applied mathematics, engineering, or computer science. Familiarity with optimization solvers (like Gurobi or CPLEX), programming languages (such as Python, C++, or MATLAB), and modeling frameworks (e.g., Pyomo, AMPL) is essential, along with experience in data analysis and algorithm development. Critical thinking, problem-solving, and clear communication are crucial soft skills for collaborating effectively and translating complex mathematical concepts to practical solutions. These skills and qualities ensure robust and efficient solutions to complex optimization problems across diverse industries.

What is the difference between Mathematical Optimization Engineer vs Data Scientist?

AspectMathematical Optimization EngineerData Scientist
Required CredentialsDegree in Mathematics, Operations Research, or related fields; often certifications in optimization toolsDegree in Computer Science, Statistics, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on developing algorithms for optimization problems in industries like logistics, manufacturing, financeAnalyze large datasets to extract insights, build predictive models, and support decision-making
Employer & Industry UsageUsed in supply chain, finance, energy sectors for process improvementUsed across tech, marketing, healthcare, finance for data-driven decision making

While both roles involve analytical skills and programming, Mathematical Optimization Engineers specialize in creating algorithms to solve complex optimization problems, whereas Data Scientists focus on analyzing data to inform business decisions. The roles often overlap in industries like finance and tech but serve different core functions.

What are popular job titles related to Mathematical Optimization Engineer jobs?

For Mathematical Optimization Engineer jobs, the most frequently searched job titles are:

Infographic showing various Mathematical Optimization Engineer job openings in the United States as of September 2026, with employment types broken down into 71% Full Time, and 29% Contract. Highlights an 86% In-person, and 14% Remote job distribution.

Senior Optimization Engineer

San Francisco, CA

$123K - $169K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 11 days ago


Key responsibilities

  • Lead the development of optimization solutions from mathematical formulation through production deployment.

  • Design and implement mathematical optimization models for energy systems, including electricity markets and battery energy storage.

  • Write clean, well-tested Python code and contribute to the deployment of optimization services into production systems.


Job description

Location: San Francisco, CA (Hybrid)

What is Verse? 

The race to AI has become the race to power. Every breakthrough in artificial intelligence depends on one thing: access to electricity. But across the country, aging grid infrastructure and years-long interconnection queues are slowing the deployment of the data centers that will power the next generation of innovation. Solving this challenge isn't just about energy—it's about unlocking the future of AI.

At Verse, we're building the energy intelligence platform for the AI economy. Our software helps the world's largest energy consumers achieve faster, cheaper, and cleaner power by combining real-time control of energy assets with complete visibility into their energy portfolio. Backed by Bessemer Venture Partners, GV, Coatue, and NVIDIA, and built by pioneers in grid-scale batteries, energy markets, and enterprise software, we're redefining how the world's most ambitious organizations access and manage energy.

The Role

We're seeking an experienced Senior Optimization Engineer to join our Data Science Team. In this role, you will lead the design, development, and deployment of optimization models that power our software platform across applications including electricity markets, renewable energy, and battery energy storage systems.

You will be responsible for developing production-grade optimization engines that solve complex operational and planning problems at scale. This role requires deep expertise in mathematical optimization, strong software engineering skills in Python, and experience building optimization models that integrate with production systems. The ideal candidate has significant experience in the energy industry, particularly electricity markets and battery storage optimization.

This position emphasizes technical leadership, ownership of complex optimization projects, and collaboration across engineering, product, and commercial teams to deliver high-impact optimization solutions.

Key Responsibilities

  • Lead End-to-End Optimization Engineering: Lead the development of optimization solutions from mathematical formulation through production deployment. Translate business requirements into scalable optimization models and implement them as robust, maintainable Python software. Design reusable optimization frameworks and services that integrate seamlessly into Verse's cloud platform and support long-term product development
  • Optimization Modeling & Solver Development: Design and implement mathematical optimization models using linear, mixed-integer, quadratic, and related optimization techniques. Formulate robust models for scheduling, dispatch, planning, and operational decision-making, and improve solver performance through model reformulation, decomposition methods, warm starts, heuristics, and parameter tuning to ensure scalable, production-ready optimization solutions.
  • Energy Systems Modeling: Develop optimization models for electricity markets, battery energy storage systems, renewable energy assets, and other distributed energy resources. Translate market rules and operational constraints into mathematically rigorous optimization formulations.
  • Software Engineering & Productionization: Write clean, well-tested, and maintainable Python code following modern software engineering best practices. Contribute to architecture decisions, testing frameworks, CI/CD pipelines, and deployment of optimization services.
  • Cross-Functional Collaboration: Partner closely with product managers, software engineers, data scientists, and commercial stakeholders to understand business requirements and deliver optimization capabilities that integrate into customer-facing products.
  • Technical Leadership: Mentor junior engineers, establish best practices for optimization modeling and software development, conduct code reviews, and help shape the technical direction of Verse's optimization platform.

What We're Looking For (Minimum Qualifications)

  • 5+ years of professional experience developing mathematical optimization models in production environments
  • Demonstrated experience independently leading complex optimization projects from formulation through deployment
  • Professional experience developing production software in Python
  • Experience deploying optimization models into scalable production systems
  • Professional experience in the energy industry is required
  • Experience with battery energy storage optimization is strongly preferred
  • Master's degree or higher in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Electrical Engineering, or a related quantitative field. A bachelor's degree with significant relevant experience may also be considered.

What Will Make You Stand Out (Preferred Qualifications)

  • Experience optimizing battery energy storage systems in wholesale electricity markets
  • Knowledge of market optimization problems involving ISO/RTO markets (CAISO, ERCOT, PJM, MISO, NYISO, ISO-NE, etc.)
  • Familiarity with stochastic optimization, robust optimization, or multi-stage optimization methods
  • Experience developing optimization services for real-time or operational decision support systems
  • Experience mentoring engineers or leading technical projects
  • PhD in Operations Research, Applied Mathematics, Industrial Engineering, or a related quantitative discipline

What makes Verse a great place to work? 

Lead with Empathy: We lift each other up with humility and kindness, always putting colleagues and customers first
Be Honest & Transparent: We prioritize effective communication to build trust with our team, customers, and stakeholders
Move with Balance & Precision: We believe speed and perseverance must be accompanied by thoughtfulness and reflection
Leave the World a Better Place: We are passionate about our mission, and we strive to create a sustainable world for future generations

Base Pay Range

$150,000-$210,000

This is the estimated base salary range for this position, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs.

Benefits and Employee Perks 

  • Competitive compensation and equity grant at a high-growth start up 
  • Comprehensive benefits package including medical, dental and vision insurance, and 401k 
  • Flexible hours and unlimited PTO 
  • Diverse and inclusive working environment 

Verse is an equal opportunity employer. All applicants and employees are considered for hire, promotion, and compensation without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, marital or familial status.