Hardware Machine Learning Engineer

Quick Facts

• Role: Hardware Machine Learning Engineer

• Focus: Architecting and deploying ML inference directly on custom FPGA/ASIC hardware

Description

You will help drive machine learning from the ground up on custom hardware, architecting solutions that account for latency budgets, resource limits, and numerical precision. You’ll co-design and implement ML models with traders, quant researchers, and engineers, working hands-on through proof-of-concept to production. You’ll also evaluate emerging research and determine which approaches translate to measurable system improvements.

Responsibilities

• 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

Requirements

• Solid understanding of hardware constraints and design trade-offs for efficient mapping of ML onto FPGA or custom ASICs (e.g., pipelining, resource utilization, fixed-point arithmetic)

• Experience with hardware fundamentals via VHDL/SystemVerilog development, HLS tools, or ML-to-hardware frameworks (e.g., 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

Benefits

• Base salary range: $200,000–$225,000 USD

• Bonus & benefits: discretionary bonus (for full-time, permanent positions) and benefits including paid leave and insurance