Machine Learning Compiler Engineer

We are working with a leading global trading firm to deploy machine learning directly onto custom hardware, and we're seeking engineers and researchers to help build this capability from the ground up. This initiative offers a rare opportunity to design end‑to‑end ML systems-spanning model representation, compiler infrastructure, and hardware execution-operating in one of the most performance‑critical computing environments in the world. The team owns the full technology stack, from silicon through compilers and runtime systems. This end‑to‑end ownership enables deep optimization and rapid iteration: when performance bottlenecks arise, they can be addressed directly at the compiler, system, or hardware level. If you are motivated by pushing the limits of latency, throughput, and efficiency in real‑world ML systems, this role offers both technical depth and immediate impact. Candidates from systems, compilers, hardware, or applied ML backgrounds are encouraged to apply. Prior experience in trading or finance is helpful but not required. Core Responsibilities Design, implement, and optimize ML compiler pipelines that lower high‑level models into efficient, hardware‑aware execution for custom accelerators, FPGAs, or ASICs Treat hardware constraints-latency budgets, memory bandwidth, resource utilization, and numerical precision-as first‑class considerations throughout the compilation process Collaborate closely with ML researchers, systems engineers, and hardware architects to co‑design models, compiler IRs, and hardware interfaces Develop compiler passes for quantization, operator fusion, scheduling, memory layout, and parallelization Translate model and workload requirements into actionable insights that inform both compiler architecture and future hardware design Prototype, benchmark, and deploy ML inference pipelines from proof‑of‑concept through production environments Track and evaluate emerging research in ML compilers, machine learning systems, and quantization, identifying techniques that lead to measurable system‑level improvements Skills and Experience Strong foundation in compiler or systems engineering, with an emphasis on performance optimization and hardware targets Experience mapping ML workloads onto constrained or latency‑sensitive hardware environments Familiarity with ML compiler infrastructure such as

MLIR, TVM, XLA

, or similar frameworks Experience with quantization, fixed‑point arithmetic, or reduced‑precision inference Proficiency in Python, C++, or similar languages for compiler development, tooling, testing, and benchmarking Solid understanding of machine learning fundamentals, including neural network architectures and inference optimization Strong communication skills and the ability to collaborate across research, systems, and hardware teams Nice to Have Experience integrating ML compilers with hardware backends such as FPGAs, custom accelerators, or ASICs Exposure to hardware design or ML‑to‑hardware flows (e.g., HLS tools, RTL, hls4ml, FINN, Vitis AI) Background in latency‑critical or resource‑constrained systems such as high‑performance trading systems, real‑time signal processing, scientific instrumentation, or HPC Familiarity with functional verification or simulation methodologies (e.g., SystemVerilog, UVM, Cocotb) Advanced degree (MS or PhD) in Computer Science, Electrical Engineering, Physics, or a related field-or equivalent industry or research experience