ZipRecruiter

Log In

1

Combinatorial Optimization Jobs in California (NOW HIRING)

Advantest

Applied AI Engineer

San Jose, CA · On-site

Strong fundamentals in algorithms, graph methods, search, combinatorial optimization, computational geometry, or constraint solving. * Experience solving structured optimization problems such as ...

Advantest

Applied AI Engineer

San Jose, CA

Strong fundamentals in algorithms, graph methods, search, combinatorial optimization, computational geometry, or constraint solving. * Experience solving structured optimization problems such as ...

pony.ai

Software Engineer Intern - Generalist

Fremont, CA · On-site

$7.0K - $10K/mo

Strong foundation in mathematics and theoretical computer science, such as linear algebra, calculus, graph theory, computational geometry, combinatorial optimization algorithms, stochastic processes ...

pony.ai

Software Engineer Intern - Generalist

Fremont, CA · On-site

$7.0K - $10K/mo

Strong foundation in mathematics and theoretical computer science, such as linear algebra, calculus, graph theory, computational geometry, combinatorial optimization algorithms, stochastic processes ...

pony.ai

Software Engineer Intern - Generalist

Fremont, CA · On-site

$7.0K - $10K/mo

Strong foundation in mathematics and theoretical computer science, such as linear algebra, calculus, graph theory, computational geometry, combinatorial optimization algorithms, stochastic processes ...

pony.ai

Software Engineer - Evaluation

Fremont, CA · On-site

$120K - $200K/yr

Strong foundation in mathematics and theoretical computer science, such as linear algebra, calculus, graph theory, computational geometry, combinatorial optimization algorithms, stochastic processes ...

pony.ai

Software Engineer - Evaluation

Fremont, CA · On-site

$120K - $200K/yr

Strong foundation in mathematics and theoretical computer science, such as linear algebra, calculus, graph theory, computational geometry, combinatorial optimization algorithms, stochastic processes ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

pony.ai

Software Engineer - Evaluation

Fremont, CA · On-site

$120K - $200K/yr

Strong foundation in mathematics and theoretical computer science, such as linear algebra, calculus, graph theory, computational geometry, combinatorial optimization algorithms, stochastic processes ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

SLB

High Performance Computing Engineer

Sunnyvale, CA · On-site

Apply advanced operations research techniques using linear programming, quadratic programming, and combinatorial optimization. Develop and deploy heuristic and metaheuristic approaches (e.g ...

next page

Showing results 1-20

All JobsCombinatorial Optimization JobsCombinatorial Optimization Jobs in California

Combinatorial Optimization information

See California salary details

$41K

$140.6K

$198.4K

How much do combinatorial optimization jobs pay per year?

As of Aug 30, 2026, the average yearly pay for combinatorial optimization in California is $140,595.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,900.00 and $164,300.00 per year, depending on experience, location, and employer.

What is combinatorial optimization?

Combinatorial optimization is a field in mathematics and computer science focused on finding the best solution from a finite set of possible solutions. It involves problems where you need to arrange, select, or group discrete objects according to certain rules to achieve an optimal outcome. Examples include scheduling, routing, and assignment problems. Techniques such as linear programming, branch and bound, and heuristics are often used to solve these problems. Combinatorial optimization is widely applied in logistics, operations research, computer science, and engineering.

What are the key skills and qualifications needed to thrive as a combinatorial optimization specialist?

To thrive as a Combinatorial Optimization Specialist, you need a solid background in mathematics, computer science, and operations research, often supported by an advanced degree in a related field. Familiarity with programming languages (such as Python, C++, or Java), optimization libraries, and mathematical modeling tools like CPLEX or Gurobi is typically required. Strong analytical thinking, problem-solving skills, and effective communication help you devise and explain complex solutions to stakeholders. These skills are crucial for developing efficient algorithms and models that address challenging optimization problems in various industries.

How does a combinatorial optimization specialist typically collaborate with other departments within an organization?

Combinatorial Optimization specialists frequently work cross-functionally, partnering with data scientists, software engineers, and business analysts to translate complex business problems into mathematical models. They help teams identify optimal solutions for scheduling, routing, resource allocation, and other operational challenges. Effective communication is crucial, as specialists must explain complex algorithms to non-technical stakeholders and integrate their solutions into broader business processes. Collaborative teamwork and iterative problem-solving are common in this role.

What is the difference between Combinatorial Optimization vs Data Analyst?

AspectCombinatorial OptimizationData Analyst
Required CredentialsMathematics, Operations Research, Computer Science degreesStatistics, Data Science, Business Analytics degrees
Work EnvironmentResearch labs, consulting firms, tech companiesCorporate offices, finance, marketing departments
Industry UsageLogistics, manufacturing, AI, supply chainFinance, marketing, healthcare, retail

While both roles involve analytical skills, Combinatorial Optimization focuses on solving complex mathematical problems to find optimal solutions, often in logistics and operations. Data Analysts interpret data to inform business decisions, working across various industries. Understanding these differences helps clarify career paths and employer expectations.

What are popular job titles related to Combinatorial Optimization jobs in California?

For Combinatorial Optimization jobs in California, the most frequently searched job titles are:

What job categories do people searching Combinatorial Optimization jobs in California look for?

The top searched job categories for Combinatorial Optimization jobs in California are:

What cities in California are hiring for Combinatorial Optimization jobs?

Cities in California with the most Combinatorial Optimization job openings:

Combinatorial Optimization jobs near you
Infographic showing various Combinatorial Optimization job openings in California as of August 2026, with employment types broken down into 91% Full Time, 7% Part Time, and 2% Contract. Highlights an 77% Physical, 5% Hybrid, and 18% Remote job distribution, with an average salary of $140,595 per year, or $67.6 per hour.

Applied AI Engineer

San Jose, CA • On-site

Advantest
Manufacturing • 1 - 5K employees
Learn more about Advantestexternal

Full-time

Re-posted 19 days ago

Job description

Position Overview:
We are seeking highly skilled Applied AI Engineer (Software Engineer) to build intelligent systems that automate, optimize and validate PCB design workflows.
The person will work at the intersection of electronics engineering, EDA tools and AI to significantly reduce design cycle time, improve quality and enable next-generation autonomous PCB design capabilities.
The role involves working with large-scale datasets, reinforcement learning, optimization, algorithms, building predictive and deploying ML/AI solutions for complex PCB Design workflows.
What You Will Do
  • Collect, clean and preprocess structured and unstructured data from multiple sources (EDA software etc.)
  • Build working prototypes for AI-assisted PCB Design automation.
  • Design, train and evaluate supervised, unsupervised and RL (reinforcement learning) machine learning models.
  • Implement models such as regression, classification, clustering, time series, GNNs, reinforcement learning and optimization algorithms
  • Formulate automation task as an optimization/RL problem, including state representation, action space, reward design, constraints and evaluation criteria.
  • Evaluate and implement different algorithms such as simulated annealing, greedy approach, graph-based, force-directed methods, constraint solving, ILP/CP-SAT or evolutionary algorithms.
  • Develop RL or learning-guided methods using realistic EDA/PCB design data.
  • Define quality metrics for evaluating layout or assignment solutions based on cost efficiency, design-rule violations, conflict minimization, density and engineering review effort.
  • Create benchmark datasets and evaluation pipelines to compare generated output against baselines and engineer-reviewed layouts.
  • Design data representations for components, nets, board regions, keep-out zones, mechanical boundaries, constraints and connectivity graphs.
  • Build visual/debug tooling to inspect output, failure cases and quality metrics.
  • Work with PCB/layout/domain experts to translate design rules and PCB Design practices (placement, routing etc.) into software constraints.
  • Contribute to the path from research prototype to usable engineering workflow.
  • Ensure AI solutions follow ethical, responsible and explainable AI practices

Required Qualifications /Skills
  • 1-3 years of hands-on software engineering applied ML, optimization, robotics planning, EDA automation, CAD automation or related experience.
  • Strong Python programming.
  • Hands-on experience with PyTorch, JAX, TensorFlow or similar ML frameworks.
  • Practical reinforcement learning experience beyond tutorials, including environment design, reward shaping, training loops, evaluation, and debugging.
  • Strong fundamentals in algorithms, graph methods, search, combinatorial optimization, computational geometry, or constraint solving.
  • Experience solving structured optimization problems such as placement, routing, scheduling, packing, assignment, layout, planning, or path optimization.
  • Ability to independently build prototypes from problem formulation through implementation and evaluation.
  • Experience designing experiments, metrics, benchmarks, and reproducible evaluation pipelines.
  • Strong debugging, testing, profiling, and code-structuring skills.
  • Ability to collaborate with domain experts and convert engineering rules into algorithmic constraints.

Good To Have
  • PCB placement, PCB layout automation, EDA routing/placement, VLSI physical design, CAD/CAM automation or design automation experience.
  • Experience with ECAD/EDA tools such as Cadence Allegro, Altium, Siemens/Mentor, Zuken, KiCa, or similar.
  • Experience with graph neural networks, imitation learning, offline RL, actor-critic methods, policy-gradient methods or hybrid RL + heuristic systems.
  • Experience with OR-Tools, CP-SAT, ILP/MIP solvers, simulated annealing, genetic algorithms, Bayesian optimization or other metaheuristics.
  • Experience with graph/netlist data, geometric layouts, spatial optimization or constraint-heavy engineering data.
  • Experience with Ray/RLlib, Stable-Baselines3, CleanRL, Gymnasium or custom RL environments.
  • GPU training, distributed experimentation, experiment tracking or scalable model evaluation experience.
  • Bachelors/master's in computer science, Electrical Engineering, Robotics, AI/ML, Applied Mathematics, Operations Research, or related field.

Ideal Candidate Backgrounds
  • Senior ML engineer with real reinforcement learning or combinatorial optimization experience.
  • Optimization engineer from robotics, scheduling, logistics, CAD/CAM, GIS, EDA, or spatial planning.
  • EDA/VLSI/PCB automation engineer with strong software and optimization skills.
  • Applied researcher who has shipped or prototyped working systems beyond academic experiments.
  • Software engineer who has built scalable experimental systems for structured optimization problems.

Apply