Hardware Machine Learning PhD Research Internship

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• 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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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

U.S. benchmark only; posted salary is not compared across countries or currencies.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Entry From the posting source checked Jun 20, 2026

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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