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Phd Optimization Research Jobs (NOW HIRING)

Pika

Research Intern (BS/MS/PhD)

Palo Alto, CA · On-site

We are looking for passionate Research Interns at the BS, MS, or PhD level with significant ... Work directly on large-scale mid-training, post-training, and optimization efforts for Videogen ...

PIKA Inc

Research Intern (BS/MS/PhD)

Palo Alto, CA · On-site

We are looking for passionate Research Interns at the BS, MS, or PhD level with significant ... Work directly on large-scale mid-training, post-training, and optimization efforts for Videogen ...

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This role is designed for new PhD graduates or early‑career researchers interested in applying ... than optimization of new machine learning methodologies. This role can be remote within the U.S ...

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Phd Optimization Research information

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How much do phd optimization research jobs pay per year?

As of Sep 10, 2026, the average yearly pay for phd optimization research in the United States is $95,315.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,000.00 and $109,000.00 per year, depending on experience, location, and employer.

What is a PhD in optimization research?

A PhD in Optimization Research is an advanced academic degree focused on developing and analyzing mathematical models and algorithms to find the best possible solutions to complex problems. This field often involves linear and nonlinear programming, combinatorial optimization, and stochastic processes, and is applied in areas such as operations research, machine learning, logistics, and engineering. Graduates are prepared for careers in academia, industry, or research institutions, where they work on improving decision-making processes and resource allocation. The program typically involves coursework, comprehensive exams, and original research leading to a dissertation.

What are the typical collaborative projects that a PhD optimization researcher might work on within a multidisciplinary team?

PhD Optimization Researchers often collaborate on projects that integrate expertise from fields such as data science, engineering, computer science, and business analytics. These projects may involve developing and implementing advanced optimization algorithms to solve complex, real-world problems like supply chain management, resource allocation, or energy systems modeling. Team members typically contribute domain knowledge, data, and problem requirements, while the optimization researcher focuses on model formulation, algorithm selection, and solution analysis. Effective communication and adaptability are essential, as researchers must translate technical findings into actionable insights for stakeholders.

What are the key skills and qualifications needed to thrive as a PhD optimization researcher, and why are they important?

To excel as a PhD Optimization Researcher, you typically need a doctorate in applied mathematics, computer science, operations research, or a related field, along with expertise in mathematical modeling and algorithm development. Proficiency with programming languages such as Python, MATLAB, or C++, and familiarity with optimization libraries and tools like Gurobi or CPLEX are commonly required. Strong analytical thinking, creativity, and effective communication skills help in formulating novel solutions and collaborating with interdisciplinary teams. These competencies are crucial for advancing research, solving complex optimization problems, and effectively disseminating findings within both academic and industry settings.

What is the difference between Phd Optimization Research vs Data Scientist?

AspectPhd Optimization ResearchData Scientist
Required CredentialsPhD in Operations Research, Applied Mathematics, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; some roles prefer PhD
Work EnvironmentResearch labs, academia, R&D departments in industryTech companies, finance, healthcare, consulting firms
Industry UsageFocus on developing optimization algorithms, mathematical modelingFocus on data analysis, machine learning, predictive modeling
Common Search/ComparisonYesYes

While both roles involve advanced analytical skills, Phd Optimization Research primarily focuses on developing and refining optimization algorithms and mathematical models, often in research or academic settings. Data Scientists analyze large datasets to extract insights and build predictive models, often applying machine learning techniques. The roles overlap in data analysis and quantitative skills but differ in their core focus and typical work environments.

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What are popular job titles related to Phd Optimization Research jobs?

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Phd Optimization Research jobs near you
Infographic showing various Phd Optimization Research job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 88% Full Time, 9% Part Time, and 1% Contract. Highlights an 79% Physical, 3% Hybrid, and 18% Remote job distribution, with an average salary of $95,315 per year, or $45.8 per hour.

Research Engineer / Scientist - Optimization

New York, NY • On-site

Percepta
Strategic Planning Consulting Services • 1 - 5K employees
Learn more about Perceptaexternal

Full-time

Re-posted 2 days ago

Percepta rating

5.9

Company rating: 5.9 out of 10

Based on 25 frontline employees who took The Breakroom Quiz

38th of 72 rated call and contact centers

Job description

Who We Are
Percepta's mission is to transform critical institutions with applied AI. We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology.
To make that happen, we embed with industry-leading customers to drive AI transformation. We bring together:
  • Forward-deployed expertise in engineering, product, and research
  • Mosaic, our in-house toolkit for rapidly deploying agentic workflows
  • Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more

Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day to day lives.
Percepta is a direct partnership with General Catalyst, a global transformation and investment company.
About the role
As a Research Engineer/Scientist (Optimization) at Percepta, you'll work at the intersection of AI research and real-world impact. You will advance the frontier of decision-making for critical industries by combining modern machine learning with rigorous optimization research. You'll collaborate closely with our Embedded Product Managers (EPMs) and engineers to ensure our most sophisticated decision systems are both innovative and pragmatically useful in transforming how companies operate.
Responsibilities
  • Set and drive ambitious research programs that expand what's achievable in data-driven decision-making.
  • Invent new optimization-and-ML methods for high-impact problems such as planning, scheduling, routing, pricing, and inventory.
  • Build high-fidelity simulators and rigorous benchmarks that mirror real-world constraints, uncertainty, and multi-objective trade-offs.
  • Bridge research into practice by partnering with our engineers to rapidly prototype solutions and implement successful research ideas.

You may be a good fit if you
  • Hold a degree in Computer Science, Operations Research, Industrial Engineering, or Applied Mathematics (MS/PhD preferred) or have equivalent research/ industry experience.
  • Have depth in operations or mathematical optimization (LP/MIP/MINLP, CP, stochastic/robust optimization, causal inference).
  • Have experience in novel machine learning techniques for Operations Research.
  • Are comfortable implementing and debugging large-scale optimization systems.
  • Are motivated by impact in critical industries including healthcare, supply chains, energy, and finance.
  • Have a proven track record of execution.
  • Are an excellent communicator with both technical and non-technical stakeholders.
  • Enjoy extreme ownership.
  • Are passionate about AI's transformative potential.

We're working against an incredibly ambitious mission. It won't be easy but it will likely be the most fulfilling work of your career. If that excites you, let's chat, even if you don't meet all of the qualifications above.
We're working against an incredibly ambitious mission. It won't be easy, but it will likely be the most fulfilling work of your career. If this excites you, let's chat, even if you don't meet all of the qualifications above.
Our Values
Dream bigger: We have the unique privilege of taking on the most ambitious problems and we should chase them with optimism, responsibility, and genuine belief that we can make it happen. We have to embrace the hard things when no one else will.
Heart in the game: What we're doing matters and we have to give a shit. Internally, that means fixing badness when you find it. Externally, it means honoring the trust our customers place in us with their most important problems. This isn't a 9-5, nor is it a job we're ever going to monitor your hours. We promise to put work in front of you that matters and in return, we ask you to promise to care.
Win for the customer: Everyone is an engineer and the job of an engineer is to deliver outcomes, not outputs. Everything we do-the products we build, the partnerships we launch, the strategy we set-exists to make our customers successful. Delivery is the strategy.
Make the call: Organizations are only as strong as the pace at which they make decisions. Everyone at Percepta should feel empowered to commit and shape the ambiguity in front of them. But "make the call" cuts both ways: make the decision and make the phone call. High-agency decision-making only works with high-bandwidth communication and we commit to never operate in silos.
Intensity with kindness: We believe in excellence in execution, candor in feedback, ruthlessness in prioritization, and survivalist urgency. We also believe you don't need to be an asshole to deliver on any of this. The trust built through shared kindness and vulnerability is what makes the intensity sustainable.

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