Summer 2026 Intern - Applied Scientist, Optimization
Salesforce2 LocationsPosted 27 March 2026
Job Description
To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
About Futureforce University Recruiting
Our Futureforce University Recruiting program is dedicated to attracting, retaining and cultivating talent. Our interns and new graduates work on real projects that affect how our business runs, giving them the opportunity to make a tangible impact on the future of our company. With offices all over the world, our recruits have the chance to collaborate and connect with fellow employees on a global scale. We offer job shadowing, mentorship programs, talent development courses, and much more.
Job Category
Fixed Term & Temporary
Job Details
About Salesforce
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
As our PhD Intern, Applied Scientist - Optimization , you will support core innovation for our industry-leading products and services, taking part in a pivotal strategic initiative to transform our static optimization pipelines into a dynamic, self-learning platform. This transformation is critical to improving our operational efficiency and enabling our systems to intelligently adapt to real-time market trends.
The systems you contribute to will help manage millions of service appointments daily across thousands of our most critical global customers, powering a platform with over $1 billion in revenue. This is a unique opportunity to apply your academic research to a project driving measurable efficiency gains that translate directly into substantial customer value.
Your primary project will be to support research on applying state-of-the-art optimization methodologies to enhance our optimization service’s scalability, robustness, and dynamic capabilities. You will collaborate with senior engineers on how we research, design, and build systems that must balance the competing demands of predictive accuracy, computational scalability, and real-time decision-making.
This is a hands-on research internship focused on technical excellence and innovation. You will be mentored by a talented team of engineers and scientists, contributing to the end-to-end technical strategy from novel research to production deployment.
What You'll Do
Apply academic theory into practice by experimenting and prototyping solutions that showcase how novel algorithms can be used to address the challenges that our system faces today.
Contribute to the technical vision and long range plan for our optimization capabilities by conducting research and developing proofs-of-concept (POCs) .
Participate in technical discussions that help set the strategic direction for the research and development of novel optimization solutions.
Collaborate with cross-functional teams to help translate prototypes into actionable solutions.
Collaborate closely with a team of engineers and scientists, fostering a culture of innovation and continuous learning.
Required Qualifications
Currently enrolled in a Ph.D. program in Operations Research, Applied Mathematics, Computer Science, or equivalent.
Strong research experience and foundational knowledge in solving complex optimization problems through mathematical programming or metaheuristics, with an interest in blending mathematical models and machine learning techniques to solve complex industrial problems.
Proficiency working with open-source and proprietary mathematical p ... (truncated, view full listing at source)
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