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Evolutionary Computing Jobs (NOW HIRING)

Optimal Inc.

Senior Optimization Engineer

Embry Hills, GA · On-site +1

$103K - $141K/yr

... evolutionary algorithms, surrogate-based optimization, or mixed-integer optimization Experience ... computing languages Familiarity with finite element, multiphysics, or system-level engineering ...

Optimal Inc.

Senior Optimization Engineer

Warren, MI · On-site

$97K - $134K/yr

... evolutionary algorithms, surrogate-based optimization, or mixed-integer optimization Experience ... computing languages Familiarity with finite element, multiphysics, or system-level engineering ...

Optimal Inc.

Senior Optimization Engineer

Warren, MI · On-site

$97K - $134K/yr

... evolutionary algorithms, surrogate-based optimization, or mixed-integer optimization Experience ... computing languages Familiarity with finite element, multiphysics, or system-level engineering ...

Peraton Labs

Senior Principal Architect

Reston, VA · On-site

... evolutionary pathways for emerging technologies (AI/ML, zero trust, PQC, AIOps, edge computing, etc). • Lead technical solutioning sessions with capture teams, partners, and SMEs; develop and ...

Peraton Labs

Senior Principal Architect

Reston, VA · On-site

... evolutionary pathways for emerging technologies (AI/ML, zero trust, PQC, AIOps, edge computing, etc). • Lead technical solutioning sessions with capture teams, partners, and SMEs; develop and ...

Peraton

Senior Principal Architect

Reston, VA · On-site

$150 - $200/hr

Develop forward-looking architectures with evolutionary pathways for emerging technologies (AI/ML, zero trust, PQC, AIOps, edge computing, etc). * Lead technical solutioning sessions with capture ...

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Evolutionary Computing information

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$11K

$57.4K

$95.5K

How much do evolutionary computing jobs pay per year?

As of Jul 17, 2026, the average yearly pay for evolutionary computing in the United States is $57,380.00, according to ZipRecruiter salary data. Most workers in this role earn between $44,500.00 and $69,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced when implementing evolutionary computing algorithms in real-world projects?

One of the main challenges in applying evolutionary computing algorithms is balancing computational cost with solution quality, as these algorithms can be resource-intensive and require careful parameter tuning. Additionally, translating theoretical models into scalable, real-world applications often involves customizing operators and fitness functions to suit specific domains. Collaboration with domain experts is crucial to accurately define objectives and constraints, and ongoing communication with software engineers ensures efficient integration into existing systems.

What are the key skills and qualifications needed to thrive as an Evolutionary Computing Specialist, and why are they important?

To thrive as an Evolutionary Computing Specialist, you need a solid background in computer science, mathematics, and algorithm design, often supported by an advanced degree in a related field. Familiarity with programming languages (such as Python, C++, or Java), machine learning frameworks, and optimization libraries is typically required. Strong analytical thinking, problem-solving abilities, and creativity are crucial soft skills that help in developing innovative solutions. These skills enable specialists to design and implement effective evolutionary algorithms that solve complex computational problems across various domains.

What is the difference between Evolutionary Computing vs Data Scientist?

AspectEvolutionary ComputingData Scientist
Required CredentialsTypically a degree in computer science, AI, or related fields; certifications in AI or machine learningDegree in statistics, computer science, or related fields; certifications in data analysis or machine learning
Work EnvironmentResearch labs, AI development teams, academiaBusiness environments, tech companies, consulting firms
Industry UsageOptimization problems, evolutionary algorithms researchData analysis, predictive modeling, business insights
Common Search/ComparisonYesYes

While both roles involve advanced computing techniques, Evolutionary Computing focuses on algorithms inspired by natural selection for optimization, whereas Data Scientists analyze data to extract insights and build predictive models. They often collaborate but serve different primary functions within tech and research industries.

What is evolutionary computing?

Evolutionary computing is a branch of artificial intelligence that uses algorithms inspired by the process of natural selection to solve complex optimization and search problems. These algorithms, such as genetic algorithms, evolve solutions over time by mimicking biological mechanisms like mutation, crossover, and selection. Evolutionary computing is used in various fields, including engineering, economics, and robotics, to find solutions that might be difficult to obtain through traditional methods. It is especially useful for problems where the search space is vast and not easily navigable by conventional algorithms.
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What cities are hiring for Evolutionary Computing jobs? Cities with the most Evolutionary Computing job openings:
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Evolutionary Computing jobs near you
Infographic showing various Evolutionary Computing job openings in the United States as of July 2026, with employment types broken down into 90% Full Time, 9% Part Time, and 1% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $57,380 per year, or $27.6 per hour.
Senior Optimization Engineer

Senior Optimization Engineer

Optimal Inc.

Embry Hills, GA • On-site, Remote

$103K - $141K/yr

Contractor

Posted 22 days ago

Job description

Job Description: Optimization Engineer / Senior Optimization Engineer

Seeking a highly motivated engineer or scientist with strong expertise in multi-objective optimization applied to complex engineering problems. This role will focus on developing, implementing, and deploying optimization methods to support engineering design, analysis, and decision-making across multidisciplinary applications.

Key Responsibilities
Develop and apply multi-objective optimization methods for engineering problems involving tradeoffs among performance, cost, mass, durability, efficiency, or other attributes
Build, validate, and improve optimization models for simulation-driven and data-driven engineering applications
Formulate engineering problems as mathematical optimization models, including objectives, constraints, and decision variables
Use and integrate commercial optimization tools and solvers as well as custom-developed optimization codes
Work with cross-functional engineering teams to translate real-world design challenges into robust optimization workflows
Analyze Pareto-optimal solutions and provide engineering insights to support decision-making
Support model calibration, sensitivity studies, design space exploration, and surrogate/model-reduction approaches where appropriate
Document methods, assumptions, and results, and communicate findings clearly to technical and non-technical stakeholders

Required Qualifications
Master's or Ph.D. in Mechanical Engineering, Aerospace Engineering, Industrial Engineering, Applied Mathematics, Operations Research, Computer Science, or a related field
Strong experience applying multi-objective optimization to engineering problems
Experience in optimization model development
Experience using commercial optimization software, solvers, or frameworks
Strong understanding of mathematical optimization techniques such as gradient-based optimization, nonlinear programming, evolutionary algorithms, surrogate-based optimization, or mixed-integer optimization
Experience working with simulation-based engineering tools and computational models
Strong programming and problem-solving skills

Preferred Qualifications
Ph.D. with demonstrated research in engineering optimization or related fields
Experience with commercial optimization codes such as CPLEX, Gurobi, modeFRONTIER, HEEDS, LS-OPT, iSight, GT-SUITE optimizer, or similar tools
Experience with surrogate modeling, reduced-order modeling, or multi-fidelity optimization
Experience in automotive, CAE, crashworthiness, thermal, structural, manufacturing, or multidisciplinary engineering optimization, simulation software such as LS-DYNA, ABAQUS.
Ability to work in a collaborative environment and influence technical direction across teams

Desired Technical Skills
Multi-objective optimization and Pareto tradeoff analysis
Engineering model formulation and simulation-based optimization
Commercial and custom optimization code development
Numerical methods, statistics, and design of experiments
Python, MATLAB, C++, or similar technical computing languages
Familiarity with finite element, multiphysics, or system-level engineering models

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