IMC
Hardware Machine Learning PhD Research Internship
About this role
Join IMC's research team as a PhD intern to develop machine learning solutions optimized for custom hardware in a high-performance trading environment. You'll own a research project from conception through deployment, collaborating with hardware engineers to advance low-latency ML inference and hardware acceleration.
What you'll do
- Design and execute an ML research project tailored to real-world trading applications
- Collaborate with hardware engineers to implement, test, and deploy ML inference systems
- Evaluate emerging research in neural architecture search, quantization, and ML systems for practical applicability
- Present findings and insights to the team to advance collective understanding
- Learn hardware design fundamentals from experienced RTL developers
- Assess research through real-world performance metrics, engineering feasibility, and industry impact
What they're looking for
- PhD enrollment in Electrical Engineering, Computer Science, Physics, or related field
- Hardware constraints and design trade-offs (pipelining, resource utilization, fixed-point arithmetic)
- Hardware development tools (VHDL, SystemVerilog, HLS, hls4ml, FINN, Vitis AI)
- Machine learning fundamentals and optimization (neural networks, quantization, inference)
- ML frameworks (PyTorch, TensorFlow)
- Python or similar programming languages
- Strong communication and cross-disciplinary collaboration
Benefits
- Base salary of $225,000
- Discretionary bonus eligibility
- Paid leave
- Insurance coverage
- Access to cutting-edge research environment
- Mentorship from world-class engineers and researchers
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IMC
IMC builds trading technology and financial systems powered by software, machine learning, and hardware engineering. The company is hiring interns and graduate-level engineers and researchers across software, machine learning, and hardware disciplines to develop trading algorithms, research strategies, and collaborative technology solutions.
View all jobs at IMCLikely interview questions
- Walk us through a hardware ML project you've worked on. What were the key hardware constraints you had to optimize for, and how did they influence your model design choices?
- Describe your experience with quantization techniques. How have you approached trading off model accuracy against hardware resource utilization in a real system?