Low-Latency ML Hardware Engineer

Fintal Partners; Chicago (IL); On-site; USD 150,000 - 230,000; Full time

Job summary

Fintal Partners is seeking a Hardware Machine Learning Engineer to deploy ML directly onto custom hardware, including FPGAs or ASICs, in a high-performing trading environment.

You will architect models with traders and engineers, shape the hardware roadmap, and work hands-on with hardware teams to move from proof-of-concept to production.

Candidates should have advanced degrees and deep ML and hardware knowledge, with experience in VHDL/SystemVerilog, HLS, and ML frameworks.

Qualifications

• Understanding hardware constraints shapes how ML models map onto hardware.
• Experience with VHDL/SystemVerilog or HLS tools is required.
• Strong knowledge of neural networks and ML frameworks (PyTorch/TensorFlow).
• Proficiency in Python or C++ for tooling, testing, and simulation.
• An advanced degree in EE, CS, Physics, or a related field.

Responsibilities

• Architect and co-design ML models with traders and software engineers.
• Translate ML model requirements into concrete hardware decisions.
• Implement, verify, and deploy ML inference on custom hardware from PoC to production.
• Track research in neural architecture search, ML systems, and quantization.

Skills

Hardware design

FPGA

SystemVerilog

VHDL

HLS tools

ML frameworks

Neural networks

Python

C++

Education

MS or PhD in EE/CS/Physics

Tools

hls4ml

FINN

Vitis AI

MLIR

TVM

XLA

Job description

Fintal Partners is seeking a Hardware Machine Learning Engineer to deploy ML directly onto custom hardware, including FPGAs or ASICs, in a high-performing trading environment.

You will architect models with traders and engineers, shape the hardware roadmap, and work hands-on with hardware teams to move from proof-of-concept to production.

Candidates should have advanced degrees and deep ML and hardware knowledge, with experience in VHDL/SystemVerilog, HLS, and ML frameworks.