ZipRecruiter

Log In

1

Home Based Convex Optimization Jobs in California

Federato Technologies

Data Scientist

San Francisco, CA · Remote

$160K - $200K/yr

Company Description Federato Technologies is a Series A startup based in San Fransisco, CA looking ... Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization ...

Federato Technologies

Data Scientist

San Francisco, CA · On-site +1

$160K - $200K/yr

Company Description Federato Technologies is a Series A startup based in San Fransisco, CA looking ... Graduate work in an optimization related field (e.g RL, Convex Optimization, Bayesian Optimization ...

Inversion

Staff GNC Engineer (Guidance)

Los Angeles, CA · On-site

$161K - $221K/yr

Experience with modern convex optimization methods for guidance (e.g., successive convexification ... Individual pay will be determined on a case-by-case basis and may vary based on the following ...

CONTROLNET

Vehicle Control Research Engineer

San Diego, CA · On-site

$150 - $180/hr

Knowledge and experience in convex optimization, non‑linear optimization, stochastic optimization ... Pay Transparency Individual salaries will vary within the following range based on factors such as ...

next page

Showing results 1-20

All JobsHome Based Convex Optimization JobsHome Based Convex Optimization Jobs in California

Home Based Convex Optimization information

What is a home based convex optimization?

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.

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 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 Home Based Convex Optimization jobs in California?

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

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

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

What cities in California are hiring for Home Based Convex Optimization jobs?

Cities in California with the most Home Based Convex Optimization job openings:

Home Based Convex Optimization jobs near you

Power Systems Research Scientist

Gridmatic Inc

Cupertino, CA • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 11 days ago

Job description

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
Taking care of you today:
- Continuing Education Opportunities
- Flexible PTO
- Medical, Dental and Vision plans with competitive employer contributions
- Pre-Tax commuter benefits
- $1500/year non profit donation matching program through Millie
- Home Office Stipend
 
Protecting your future for you and your family:
- 401K contribution match up to 4%
- Company-paid parental leave
- Company Paid Life Insurance
- Stock Option Loan Program

Apply