Machine Leaning Performance Engineer (Inference)

Responsibilities:

• Benchmarking & Strategy:
  • Lead the technical evaluation of diverse inference platforms - ranging across CPUs, GPUs, and FPGAs - to guide infrastructure deployment decisions.
• System Architecture Optimization:
  • Analyze and enhance execution across deep memory hierarchies to maximize resource utilization and parallel processing. You will assess and resolve memory subsystem and interconnect bottlenecks across the end-to-end inference lifecycle.
• Infrastructure & Deployment Feasibility:
  • Collaborate with Infrastructure teams to understand thermal, power, and operational constraints of hardware platforms to design inference strategies for our latency-critical trading strategies that fit within those envelopes.
• GPU Kernel Development:
  • Develop highly optimized kernels and integrate specialized performance libraries to extract maximum computational throughput from the underlying silicon.
• Model Optimization & Deployment:
  • Implement advanced model reduction techniques (quantization, pruning, distillation) to ensure compact memory footprints and numerical stability. Prioritize optimization for low-latency, event-level inference workloads to meet real-time trading requirements.
• Cross-Functional Collaboration:
  • Collaborate closely with ML Researchers, HPC Engineers, FPGA Engineers, and Datacenter Engineers to bring to fruition target deployments.

Qualifications:

• 2+ years of experience optimizing deep learning inference in latency-sensitive or high-throughput production environments, in any domain.
• ML Frameworks: Deep expertise in lower-level ML framework development (PyTorch/JAX), paired with strong Python/C++ skills and a thorough understanding of mixed-precision computation.
• Kernel Development & Optimization Tooling: Proven experience in custom GPU kernel development. Deep familiarity with advanced optimization libraries and compilers (e.g., Triton, TensorRT, ONNX, IREE, HLS4ML, cuBLAS, CUTLASS) as well as profiling tools (e.g., Nsight Systems, Nsight Compute).
• GPU Architecture Mastery: Deep expertise in GPU microarchitecture, encompassing SM execution, warp scheduling, and full memory hierarchy optimization (registers to HBM).
• Cross-Architecture Benchmarking: Proven record of rigorous, data-driven approach to evaluating inference performance across heterogeneous compute architectures.
• Bonus: Practical experience targeting and optimizing inference workloads on specialized hardware ecosystems, including FPGAs and ASICs.
• Prior experience in financial trading is not required.

Comp: $200-300K + Bonus