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Home Based Convex Optimization Jobs (NOW HIRING)

CONTROLNET

Vehicle Control Research Engineer

San Diego, CA · On-site

$150 - $180/hr

  • Life

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 ...

New

Watney

Senior Controls Engineer

San Francisco, CA · On-site

$110 - $150/hr

... convex optimization. We're committed to building a diverse, inclusive team. At Watney Robotics, we welcome people of all backgrounds and identities, and we make hiring decisions based on skills ...

New

Inversion

Staff GNC Engineer (Guidance)

Vista, 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 ...

Corning

Sr. Engineer, Advanced Process Controls

Painted Post, NY · On-site

$82K - $108K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

You can help connect the unconnected, drive the future of automobiles, transform at-home ... Foundation in numerical optimization (LP, NLP, MILP, convex optimization) and ability to formulate ...

Corning

Sr. Engineer, Advanced Process Controls

Painted Post, NY · On-site

$82K - $108K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

You can help connect the unconnected, drive the future of automobiles, transform at-home ... Foundation in numerical optimization (LP, NLP, MILP, convex optimization) and ability to formulate ...

Corning

Sr. Engineer, Advanced Process Controls

Manhattan, NY · On-site

$106K - $141K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

You can help connect the unconnected, drive the future of automobiles, transform at-home ... Foundation in numerical optimization (LP, NLP, MILP, convex optimization) and ability to formulate ...

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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 Aug 20, 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 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.

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Home Based Convex Optimization jobs near you
Infographic showing various Home Based Convex Optimization job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 14% Part Time, and 7% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $55,794 per year, or $26.8 per hour.

Engineering Manager, State Estimation

Hayden AI

San Francisco, CA • On-site

$240K - $310K/yr

Full-time

Re-posted 5 days ago

Job description

About Us
At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges.
From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future.
Job Summary:
The Engineering Manager, State Estimation will lead a high-performing team focused on developing advanced mapping, localization, and SLAM (Simultaneous Localization and Mapping) solutions for embedded camera systems. You will oversee the research, development, and deployment of large-scale mapping and localization solutions, while shaping the technical roadmap and ensuring successful collaboration across device, cloud, and applied AI teams.
Responsibilities:
  • Build, mentor, and manage a team of engineers developing state-of-the-art mapping, localization, and SLAM algorithms.
  • Set technical direction and project priorities, aligning them with broader organizational goals.
  • Drive high-impact, cross-functional projects from conception through deployment, coordinating with research, hardware, and product teams.
  • Provide technical guidance on algorithm design, system architecture, and implementation, while ensuring best practices in software development and performance optimization.
  • Foster a culture of innovation, technical rigor, and collaboration across the team.
  • Develop and maintain project roadmaps, milestones, and technical documentation.
  • Partner with senior leadership to define long-term strategies for localization, state estimation, and multi-sensor calibration.
  • Review and approve designs, architectures, and implementations, ensuring technical excellence and scalability.
  • Lead recruitment, hiring, and performance management to grow a world-class engineering team.

Required Qualifications:
  • Bachelor of Science degree (M.S. or Ph.D. preferred) in Electrical and Computer Engineering, Robotics, Machine Learning, Computer Science, or a related field.
  • 10+ years of industry experience (8+ years with an M.S., 6+ years with a Ph.D.), including significant leadership or management responsibilities.
  • Proven experience leading engineering teams working on state estimation, localization, or SLAM technologies.
  • Strong foundation in C++ development, geometric computer vision, stochastic processes, and nonlinear/convex optimization.
  • Deep understanding of filtering algorithms (e.g., Kalman, particle filters) and optimization-based methods (e.g., nonlinear least squares).
  • Familiarity with camera geometry, structure from motion, multi-sensor fusion, factor graphs, and related state estimation concepts.
  • Strong track record of technical execution combined with leadership and team-building skills.
  • Exceptional communication skills, with the ability to collaborate effectively across engineering, research, and product functions.
  • Experience in technology transfer and delivering research innovations into production systems.

Preferred Qualifications:
  • Demonstrated success deploying SLAM/VIO estimators in real-world applications.
  • Experience with multi-sensor systems, including GPS, IMU, camera, and wheel odometry.
  • Exposure to integrating deep learning with classical state estimation approaches.
  • Strong record of published research or contributions to the robotics and computer vision community.
  • Experience scaling and leading multi-disciplinary engineering teams

Apply