Machine Learning Engineer

Overview

Hardware Machine Learning Engineer to architect and deploy ML inference directly on custom hardware.

What you'll do

• Architect and co-design ML models with traders, quant researchers, and software engineers.
• Treat hardware constraints (latency budgets, resource limits, numerical precision) as first-class design inputs.
• Translate ML model requirements into architectural decisions to shape a custom hardware roadmap.
• Implement, verify, and deploy ML inference solutions from proof-of-concept through production.
• Track and evaluate research in neural architecture search, learning systems, and quantization methods.
• Determine what research work translates into measurable improvements in systems.

What you'll need

• Solid understanding of hardware constraints and design trade-offs (e.g., pipelining, resource utilization, fixed-point arithmetic) for mapping onto FPGAs or custom ASICs.
• Hardware fundamentals experience via VHDL/SystemVerilog, HLS tools, or ML-to-hardware frameworks like hls4ml, FINN, or Vitis AI.
• Machine learning fundamentals in neural network architectures, inference optimization, and quantization.
• ML framework experience with PyTorch or TensorFlow.
• Proficiency in Python, C++, or similar languages for tooling, testing, and simulation.
• Strong communication skills for collaboration across technical and non-technical teams.

Nice to have

• Exposure to ML compiler infrastructure such as MLIR, TVM, or XLA.
• Background in latency-sensitive or resource-constrained systems (examples listed include high-frequency trading, particle physics data acquisition, and real-time signal processing).
• Familiarity with functional verification methodologies (examples listed include SystemVerilog, UVM, and Cocotb).
• Advanced degree (MS or PhD) in EE, CS, Physics, or related field, or equivalent industry/research depth.

Details

• Base salary range: $200,000–$225,000 USD.
• Role described as full-time and permanent; discretionary bonus and benefits mentioned.