Hardware Machine Learning PhD Research Internship

IMC Financial Markets; Chicago (IL); On-site; USD 202,500 - 247,500; Full time

Job summary

IMC Financial Markets is offering a PhD internship in Chicago focused on machine learning and hardware acceleration. Interns will work on a research project that impacts IMC's low-latency ML inference initiatives. Responsibilities include developing ML solutions, collaborating with hardware engineers, and presenting findings. Candidates must be enrolled in a PhD program related to engineering or computer science, with a solid understanding of hardware design and machine learning fundamentals. The position offers a competitive base salary of $225,000.

Qualifications

• Currently enrolled in a PhD program in a related field.
• Solid understanding of design trade-offs for ML models.
• Experience with hardware development and ML-to-hardware frameworks.

Responsibilities

• Develop an ML-focused research project in a trading environment.
• Collaborate with hardware engineers to implement ML inference.
• Evaluate emerging research for measurable improvements.

Skills

Hardware constraints understanding

Machine learning fundamentals

Proficiency in Python

Strong communication skills

Education

PhD in Electrical Engineering, Computer Science, Physics or related field

Tools

VHDL/SystemVerilog

HLS tools

ML frameworks (PyTorch/TensorFlow)

Job description

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 utilization, fixed-point arithmetic) that shape how ML models can be efficiently mapped onto FPGAs or custom ASICs
• Experience with hardware fundamentals, whether through VHDL/SystemVerilog development, HLS tools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI
• Understanding of machine learning fundamentals - neural network architectures, inference optimization, quantization techniques, ML frameworks such as PyTorch/TensorFlow
• Proficiency in Python or similar languages for tooling, testing, and simulation
• Strong communication skills and ability to work collaboratively across disciplines with both technical and non-technical teams

Base Salary: $225,000