Summer 2026 Intern - Applied Scientist,… at Salesforce · Boston

Summary: Salesforce is the #1 AI CRM, dedicated to driving innovation and customer success. As a PhD Intern in Applied Science - Optimization, you will support the transformation of optimization pipelines into a dynamic, self-learning platform, enhancing operational efficiency and adapting to market trends. Responsibilities: - 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 programming solvers such as CPLEX and Gurobi - Experiences in one or more of the following areas: large-scale forecasting and data-driven decision making - Strong communication and collaboration skills Preferred Qualifications: - Experience with online learning or applying Reinforcement Learning (RL) specifically to optimization problems - Familiarity with emerging research areas like differentiable optimization or learning-to-optimize (L2O) frameworks - Solid grasp of probabilistic modeling, statistical evaluation, and the use of simulation-based testing to validate complex models and systems - Proficiency in modern ML frameworks like PyTorch or TensorFlow - Prior experience working within cross-functional teams that include data scientists, operations research specialists, and software engineers Required Skills: Optimization methodologies, Mathematical programming, Machine learning techniques Important Skills: CPLEX, Gurobi, Data-driven decision making Nice-to-Have Skills: Reinforcement Learning, Probabilistic modeling, Statistical evaluation, Simulation-based testing, PyTorch, TensorFlow, Collaboration skills Internship Start Date: Start in 2026 Summer Benefits: Time off programs, Medical, Dental, Vision, Mental health support, Paid parental leave, Life and disability insurance, 401(k), Employee stock purchasing program