Hardware Machine Learning Engineer

Sourced from mindpal.co

IMC is hiring a Senior Hardware Machine Learning Engineer to architect and deploy ML inference directly on custom FPGA/ASIC hardware. You will co-design ML models with traders and quant researchers, treating latency budgets, resource limits, and numerical precision as first-class constraints. The role spans proof-of-concept through production and involves evaluating emerging research in neural architecture search, ML systems, and quantization.

• Architect and co-design ML models with traders and quant researchers, treating hardware constraints as first-class inputs
• Translate ML model requirements into architectural decisions that shape the hardware roadmap
• Work with hardware engineers to implement, verify, and deploy ML inference solutions
• Track and evaluate emerging research in neural architecture search, ML systems, and quantization methods

• Solid understanding of hardware constraints and design trade-offs for mapping ML onto FPGA or custom ASICs (pipelining, resource utilization, fixed-point arithmetic)
• Experience with hardware fundamentals via VHDL/SystemVerilog, HLS tools, or ML-to-hardware frameworks (hls4ml, FINN, Vitis AI)
• Understanding of ML fundamentals including neural network architectures, inference optimization, and quantization
• Proficiency in Python, C++, or similar languages for tooling, testing, and simulation
• Strong communication skills and ability to collaborate across disciplines

• Experience with ML compilers such as MLIR, TVM, or XLA
• Familiarity with heterogeneous computing and latency optimization
• Prior work in trading or low-latency systems