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Combinatorial Optimization Jobs (NOW HIRING)

Oxy

AI Optimization Engineer

Houston, TX · On-site

Apply advanced continuous and combinatorial optimization, control, artificial intelligence, machine learning, and algorithmic techniques to build, maintain, and improve on decision systems/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 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 ...

Roberts Recruiting

Senior Data Scientist

Boston, MA

Experience with MILP, combinatorial optimization, metaheuristics, and other types of optimization are an added advantage. * Experience with agronomy or ecology work including modeling, forecasting ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$205K - $278K/yr

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

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Combinatorial Optimization information

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$41.5K

$142.5K

$201K

How much do combinatorial optimization jobs pay per year?

As of Jun 27, 2026, the average yearly pay for combinatorial optimization in the United States is $142,460.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,500.00 and $166,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Combinatorial Optimization Specialist, and why are they important?

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 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.
More about Combinatorial Optimization jobs
What cities are hiring for Combinatorial Optimization jobs? Cities with the most Combinatorial Optimization job openings:
What states have the most Combinatorial Optimization jobs? States with the most job openings for Combinatorial Optimization jobs include:
What job categories do people searching Combinatorial Optimization jobs look for? The top searched job categories for Combinatorial Optimization jobs are:
Combinatorial Optimization jobs near you
Infographic showing various Combinatorial Optimization job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 24% Physical, 1% Hybrid, and 75% Remote job distribution, with an average salary of $142,460 per year, or $68.5 per hour.
Senior Machine Learning Engineer II, Fulfillment, Matching and Positioning

Senior Machine Learning Engineer II, Fulfillment, Matching and Positioning

Instacart

Remote

$107K - $146K/yr

Full-time

Posted 22 days ago

Instacart rating

7.0

Company rating: 7.0 out of 10

Based on 30 frontline employees who took The Breakroom Quiz

32nd of 62 rated delivery companies

Job description

We're transforming the grocery industry
At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.
Instacart has become a lifeline for millions of people, and we're building the team to help push our shopping cart forward. If you're ready to do the best work of your life, come join our table.
Instacart is a Flex First team
There's no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work-whether it's from home, an office, or your favorite coffee shop-while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work.
Overview
Instacart's Logistics organization powers the intelligence and execution behind our fulfillment system. We're hiring a Senior Machine Learning Engineer to join the Matching & Positioning team, a tight-knit group of 9 engineers and scientists focused on real-time decisioning for order batching, shopper routing, and assignment across a dynamic, multi-sided marketplace.
In this role, you'll work at the intersection of operations research, combinatorial optimization, and machine learning to design and ship algorithms that directly impact profitability, on-time delivery, shopper experience, and customer satisfaction at scale. You'll collaborate closely with engineering, product, and data science partners to translate ambiguous problems into well-formed optimization and ML systems that operate under sub-second latency and high throughput.
If you thrive in a fast-paced environment, enjoy rolling up your sleeves, and want to see your models make decisions in the real world every minute of every day, this team is for you.
About the Job
You will build production-grade optimization and ML solutions that drive Instacart's fulfillment decisions end-to-end in a rapidly evolving, high-scale environment.
  • Design, implement, and deploy algorithms for order batching, real-time shopper assignment, routing, and marketplace positioning using techniques such as MIP/CP-SAT, heuristics/metaheuristics, and learning-to-rank.
  • Own the full model lifecycle: problem formulation, data pipelines and features, offline evaluation and simulation, A/B testing, staged rollouts, and ongoing monitoring/observability.
  • Build reliable, low-latency services in Python (and, where performance dictates, C++ or Go) that integrate with solvers (e.g., OR-Tools, Gurobi, CPLEX) and run on cloud infrastructure with Docker/Kubernetes.
  • Partner with product, operations, and data science to define roadmaps and success metrics; deliver measurable impact to on-time rates, shopper utilization, cost per order, and customer experience.
  • Leverage experimentation and causal methods along with offline counterfactual replay/simulation to validate changes and de-risk launches.
  • Contribute to engineering excellence through code reviews, design docs, robust testing, and participation in an on-call rotation for mission-critical fulfillment services; mentor peers and raise the technical bar.

This is a fast-moving domain with evolving constraints and objectives. Success requires comfort with ambiguity, pragmatic prioritization, and a bias toward iterative learning and continuous improvement.
About You
You pair a deep toolkit in operations research and machine learning with strong software engineering fundamentals. You're motivated by real-world impact, communicate clearly with cross-functional partners, and take ownership from ideation to production.
Minimum Qualifications
  • Bachelor's degree in Computer Science, Operations Research, Electrical Engineering, Applied Mathematics, or a related field (or equivalent practical experience).
  • 5+ years of professional experience building and shipping ML and/or optimization systems to production.
  • 3+ years formulating and solving large-scale combinatorial optimization problems (e.g., VRP, matching, scheduling) using solvers such as OR-Tools, Gurobi, or CPLEX (MIP/CP-SAT) and heuristic methods.
  • Proficiency in Python and SQL, including writing production-quality code with testing, profiling, and code review practices.
  • Hands-on experience deploying algorithms/models as microservices with Docker and Kubernetes on a major cloud provider (GCP or AWS), including monitoring, alerting, and dashboards.
  • Experience designing and operating low-latency decision services in high-throughput environments (targeting sub-second P95 response times).
  • Practical experience with A/B testing or online experimentation platforms, from hypothesis through analysis and rollout decisions.
  • Strong collaboration and communication skills with engineering, product, and data science stakeholders.
Preferred Qualifications
  • Master's or PhD in Operations Research, Computer Science, Electrical Engineering, Applied Mathematics, or a related quantitative field.
  • Domain experience in logistics, ride-hailing, delivery, or marketplace optimization at scale.
  • Familiarity with reinforcement learning or contextual bandits for online decision-making and exploration/exploitation tradeoffs.
  • Experience with geospatial data, routing APIs, and graph algorithms.
  • Background in building simulation frameworks and counterfactual evaluation for decision systems.
  • Experience with streaming data and real-time feature computation (e.g., Kafka, Flink) and feature stores.
  • Proficiency in C++ or Go for performance-critical components.
  • Track record of mentoring engineers and leading cross-functional projects to measurable outcomes.
  • Experience participating in an on-call rotation for production ML/optimization services.

#LI-Remote
Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here.
Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.
For US based candidates, the base pay ranges for a successful candidate are listed below.
CA, NY, CT, NJ
$240,000-$253,500 USD
WA
$230,000-$243,000 USD
OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI
$221,000-$233,000 USD
All other states
$201,000-$212,000 USD

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About Instacart

Sourced by ZipRecruiter

Instacart, based in San Francisco, CA, US, operates within the retail industry, specifically grocery delivery and pick-up service. It is recognized as a pioneer in this field, delivering fresh groceries from local stores directly to customers' doors. The company, which launched its services in 2012, continues to pioneer change in the online grocery shopping sector through its commitment to cutting-edge technology, new business ideas, and dedicated service.

Industry

Technology, communication and media

Company size

10,000+ Employees

Headquarters location

San Francisco, CA, US

Year founded

2012

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