ML Hardware Engineer: Custom Hardware Inference

Fintal Partners; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Fintal Partners in New York seeks an experienced Hardware Machine Learning Engineer to deploy ML directly on custom hardware, including FPGA/ASIC paths. You will architect ML models with latency budgets and work across traders, researchers, and software engineers.

You will shape hardware roadmaps, translate model requirements into architectural decisions, and implement inference solutions from proof-of-concept through production with hardware teams.

Qualifications

• Advanced degree in EE/CS/Physics or related field.
• Experience mapping ML models to hardware (FPGA/ASIC) for latency/throughput.
• Proficient in Python and C++, with exposure to ML frameworks.
• Familiar with ML compiler infrastructures and hardware-aware optimization.
• Strong communication and cross-disciplinary collaboration skills.

Responsibilities

• Architect and co-design ML models with traders and engineers, considering hardware constraints.
• Translate ML requirements into concrete architectural decisions for custom hardware.
• Deploy ML inference solutions from proof-of-concept to production with hardware teams.
• Stay current with research in neural architecture search, ML systems, and quantization.

Skills

Hardware constraint mapping

VHDL/SystemVerilog

ML frameworks (PyTorch/TensorFlow)

Python

C++

Cross-functional collaboration

Education

MS or PhD in EE/CS/Physics

Tools

hls4ml

FINN

Vitis AI

MLIR

TVM/XLA

Job description

Fintal Partners in New York seeks an experienced Hardware Machine Learning Engineer to deploy ML directly on custom hardware, including FPGA/ASIC paths. You will architect ML models with latency budgets and work across traders, researchers, and software engineers.

You will shape hardware roadmaps, translate model requirements into architectural decisions, and implement inference solutions from proof-of-concept through production with hardware teams.