Machine Learning Hardware Engineer (HW/SW Co-Design)

Job Description

A leading global quantitative trading firm is seeking a Machine Learning Hardware Engineer to help deploy machine learning directly onto custom hardware.

This is an opportunity to build from the ground up , architect solutions from scratch, and influence the technical direction of ML inference in a demanding, latency-sensitive trading environment.

The firm develops its own hardware, software, and infrastructure, giving you the scope to solve bottlenecks across the full stack. You’ll work closely with researchers, traders, and engineers to shape both the models and the hardware they run on.

Trading experience is not required. The team is open to researchers and experienced engineers from a range of technical backgrounds.

What you’ll do:

• Co-design ML models with traders, quantitative researchers, and software engineers, incorporating latency budgets, resource limits, and numerical precision into model design.
• Shape the custom hardware roadmap by translating ML requirements into architectural decisions.
• Partner with hardware engineers to implement, verify, and deploy ML inference solutions from proof of concept through production.
• Evaluate emerging research in neural architecture search, ML systems, and quantization to identify measurable improvements in performance.

What you’ll bring:

• Strong understanding of hardware constraints and design trade-offs, including pipelining, resource utilization, and fixed-point arithmetic, for mapping ML models onto FPGAs or custom ASICs.
• Experience with VHDL/SystemVerilog, HLS tools, or ML-to-hardware frameworks such as hls4ml, FINN, or Vitis AI.
• Knowledge of neural network architectures, inference optimization, quantization, and frameworks such as PyTorch or TensorFlow.
• Proficiency in Python, C++, or similar languages for tooling, testing, and simulation.
• Strong communication skills and the ability to collaborate across research, engineering, and trading teams.

Additional experience that would be valuable:

• ML compiler infrastructure such as MLIR, TVM, or XLA.
• Latency-sensitive or resource-constrained systems, including high-frequency trading, particle physics data acquisition, or real-time signal processing.
• Functional verification tools and methodologies such as SystemVerilog, UVM, or Cocotb.
• An MS or PhD in Electrical Engineering, Computer Science, Physics, or a related field, or equivalent industry or research experience.

Why this opportunity:

• Own foundational work at the intersection of machine learning and custom hardware.
• Influence model design, hardware architecture, and research direction.
• Work across an internally developed technology stack, with the ability to address performance constraints at their source.
• See your work deployed into production trading systems.