Hardware Machine Learning PhD Research Internship
IMC Trading - Chicago, United States
Posted Apr 16, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Hardware Machine Learning PhD Research Internship Chicago, United States We are deploying machine learning directly onto custom hardware - and we want you to help drive it forward. This PhD internship is an opportunity to work on research that has direct impact on IMC's work tackling open problems at the frontier of low-latency ML inference and hardware acceleration. You'll work alongside IMC engineers in one of the most demanding low-latency computing environments in the world. You'll own a focused research project from start to finish, present your findings to the team, and leave behind a prototype or benchmark that we can build on. Your Core Responsibilities - Architect and develop an ML focused research project based on a real-world trading environment - Work hands-on with hardware engineers to implement, verify, and deploy ML inference solutions - Track and evaluate emerging research in neural architecture search, machine learning systems and quantization methods, and determine what translates to measurable improvements in our systems - Present your project to the team, deepening our collective understanding of an area of ML acceleration - Gain hardware design fundamentals from skilled RTL developers and learn how they apply to our industry - Build skills to evaluate research not only from an academic perspective, but through real-world performance constraints, engineering costs, and industry impact Your Skills and Experience - Currently enrolled in a PhD program in Electrical Engineering, Computer Science, Physics, or a related field - Solid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource
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