Hardware Machine Learning Engineer

📋 Description

• Design and deploy ML engines on custom hardware for ultra-low latency.
• HW/SW co-design with traders and researchers to implement ML models.
• Shape a greenfield initiative with freedom to explore novel approaches.
• Research NN quantization, compression, and tools bridging ML to RTL.
• See production results quickly via rapid iteration and collaboration.

🎯 Requirements

• FPGA or ASIC experience with VHDL, Verilog, or SystemVerilog.
• Digital design: pipelining, timing, clock domain crossing.
• FPGA toolchains: Vivado, Quartus, Synplify.
• ML basics: neural nets, inference optimization, quantization.
• Inference for temporal models (RNNs, Transformers) on latency-sensitive hardware.
• Python or C++ for tooling, testing, simulation.

🎁 Benefits

• Discretionary bonus and comprehensive benefits.
• Paid leave and health insurance.
• Collaborative, high-performance culture with global exposure.