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Home Based Convex Optimization Jobs (NOW HIRING)

QCi

Senior Engineer, Quantum Algorithms

Hoboken, NJ · On-site

... onto entropy-based photonic quantum processors, including post-processing pipelines. * Build ... Strong mathematical background in Convex Optimization, Quadratic Programming (QP), Mixed-Integer ...

WESLEY COMMONS

Home Based Caregivers

Greenwood, SC

$13.50 - $17/hr

The Home Based Care Provider will provide continual care and assistance to the residents of Wesley ... Optimal physical and emotional health. * Adequate speech and hearing to communicate effectively ...

WESLEY COMMONS

Home Based Caregivers

Greenwood, SC · On-site +1

$13.50 - $17/hr

The Home Based Care Provider will provide continual care and assistance to the residents of Wesley ... Optimal physical and emotional health. * Adequate speech and hearing to communicate effectively ...

QCi

... onto entropy-based photonic quantum processors, including post-processing pipelines. * Build ... Strong mathematical background in Convex Optimization, Quadratic Programming (QP), Mixed-Integer ...

Cellanome

Machine Learning Engineer

Foster, OR · On-site +1

$160K - $215K/yr

This role reports to the Sr. Director AI and can be based in our San Diego CA or Foster City CA ... Familiarity with optimization and estimation techniques such as convex optimization, Kalman ...

Quantinuum

Computational Scientist - 705

Broomfield, CO · On-site

$80K - $101K/yr

Experience with numerical optimization techniques, such as convex optimization, genetic algorithms ... Actual compensation within this range may vary based on the candidate's skills, educational ...

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Home Based Convex Optimization information

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$16K

$55.8K

$102K

How much do home based convex optimization jobs pay per year?

As of Jun 19, 2026, the average yearly pay for home based 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 key skills and qualifications needed to thrive as a Home-Based Convex Optimization Specialist, and why are they important?

To excel as a Home-Based Convex Optimization Specialist, you need a strong background in mathematics, particularly linear algebra and calculus, along with experience in optimization theory and a relevant degree such as mathematics, engineering, or computer science. Proficiency with technical tools like MATLAB, Python (with libraries such as CVXPY), and optimization solvers is typically required. Critical thinking, problem-solving, and effective remote communication are essential soft skills for success in this independent, analytical role. These skills are crucial for accurately modeling, solving complex optimization problems, and collaborating efficiently with remote teams or clients.

What are some common challenges faced by professionals working in home-based convex optimization roles, and how can they be addressed?

One common challenge in home-based convex optimization roles is maintaining effective communication with team members, especially when collaborating on complex mathematical models or sharing large datasets. To address this, professionals often use collaborative tools such as cloud-based platforms and version control systems to facilitate seamless workflow and project tracking. Additionally, the solitary nature of remote work can make problem-solving more difficult, so regular virtual meetings and knowledge-sharing sessions are essential for fostering a supportive team environment. Staying updated with the latest research and optimization software also helps in overcoming technical obstacles and enhancing productivity.

What is the difference between Home Based Convex Optimization vs Data Scientist?

AspectHome Based Convex OptimizationData Scientist
Required CredentialsMathematics, Optimization, Computer Science degreesStatistics, Mathematics, Computer Science degrees
Work EnvironmentRemote, independent work on optimization problemsRemote or office, analyzing data and building models
Industry UsageFinance, tech, research institutionsTech, finance, healthcare, marketing

Home Based Convex Optimization specialists focus on solving mathematical optimization problems remotely, often within research or technical roles. Data Scientists analyze data to extract insights and build predictive models. While both roles require strong analytical skills and related credentials, their core tasks differ: one emphasizes mathematical problem-solving, the other data analysis. They are often searched together due to overlapping skills and remote work options.

What is a Home Based Convex Optimization job?

A Home Based Convex Optimization job involves working remotely to solve mathematical problems where the objective function is convex, meaning any local minimum is a global minimum. Professionals in this role typically use advanced mathematical and computational techniques to optimize processes, systems, or models across various industries, such as finance, engineering, or machine learning. Tasks may include developing algorithms, implementing optimization models, and analyzing data sets to find optimal solutions. These jobs often require a strong background in mathematics, computer science, and experience with optimization software or programming languages.
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Home Based Convex Optimization jobs near you

Power Systems Research Scientist

Gridmatic

Cupertino, CA • On-site

Full-time

Posted 29 days ago

Job description

Gridmatic is a high-growth startup and a new kind of energy company, delivering affordable, clean power by optimizing renewable energy and grid-scale batteries. With offices in the Bay Area and Houston, we bring together Silicon Valley-style innovation with deep, hands-on expertise in real-world power markets and energy retail.
As solar and wind become the fastest-growing sources of electricity, variability from weather and grid conditions makes energy prices more volatile. Gridmatic tackles this challenge with industry-leading forecasting and optimization-and gives our team the opportunity to work on problems that truly matter. Forecasting and trading energy are the foundation of what we do. We ingest large-scale data-weather, prices, load, and grid conditions-to build probabilistic machine learning forecasts that drive real operational decisions. Our work directly determines when power is bought, stored, or deployed, turning uncertainty into value for customers and the grid.
Our impact is measurable. Gridmatic is the most profitable participant in ERCOT's wholesale market and operates the top-performing battery asset in CAISO. Profitable without venture capital, we offer a collaborative, low-ego environment where rigorous thinking, autonomy, and continuous learning are core to how we work.

The Role


We are looking for a Power Systems Research Scientist to develop physics-based models of large-scale transmission systems and their impact on electricity markets.

You will work on large-scale optimization and simulation problems, including power flow, congestion, and security-constrained unit commitment and economic dispatch (SCUC/SCED). This role focuses on designing scalable algorithms and high-performance implementations for solving complex power system problems.

This role sits at the core of our research and trading stack, building models and computational tools that directly impact how we understand and operate in electricity markets.

We are particularly interested in rethinking power system optimization and simulation using modern computing (e.g., GPU acceleration).

What You'll Do:
  • Develop and analyze power network models, including AC/DC power flow, contingency analysis, and security constraints
  • Build and enhance large-scale optimization models (e.g., SCUC/SCED) with detailed transmission constraints
  • Design and implement scalable algorithms and solver components for large-scale power system optimization
  • Identify and address computational bottlenecks in network-constrained simulations and optimization
  • Model and analyze congestion and transmission-driven market outcomes
  • Simulate grid scenarios with high penetration of renewables, storage, and outages
  • Collaborate with ML and trading teams to integrate network-aware signals into forecasting and decision systems
Qualifications
  • Advanced degree (MS/PhD) in Electrical Engineering, Power Systems, or related field
  • Strong background in power systems analysis and modeling
  • Experience with power flow (AC/DC), transmission modeling, and congestion analysis
  • Familiarity with ISO/RTO markets and network-constrained market outcomes
  • Experience with optimization algorithms and large-scale mathematical programming
    • Understanding of numerical methods for convex and/or non-convex optimization
  • Strong programming skills in Python
Nice to Have:
  • Experience with tools such as PSS/E, PowerWorld, PSLF, or similar
  • Familiarity with SCUC/SCED implementations
  • Background in electricity market modeling or trading
  • Experience working with large-scale datasets and cloud applications
  • Familiarity with key power systems concepts such as PTDFs (power transfer distribution factors) and security constraints
  • Experience with GPU-accelerated computing for large-scale optimization or simulation
  • Experience with frameworks such as PyTorch or JAX for high-performance numerical computing
Join our team and make a difference! Click below or email us at [email protected].
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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