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13 дней назад

GPU Performance Engineer (CUDA)

165 000 - 300 000$
Формат работы
hybrid
Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
GPU Performance Engineer (CUDA): Building and optimizing GPU-accelerated kernels, libraries, and abstractions for financial computation workloads with an accent on CUDA programming, memory efficiency, and mixed-precision computation. Focus on profiling workloads with NVIDIA tooling, optimizing performance across next-generation hardware, and integrating multi-GPU libraries for quantitative modeling.

Location: Hybrid in NY, New York, United States, with flexible in-office days

Salary: $165,000–$300,000 base pay annually, plus potential discretionary bonus and benefits

Company

hirify.global is a quantitative investment management and trading firm applying technology, artificial intelligence, data science, and quantitative research to financial markets.

What you will do

  • Design and implement GPU-accelerated kernels for financial computation workloads.
  • Optimize GPU code for throughput, latency, and memory efficiency across current and next-generation hardware, including Blackwell and Rubin.
  • Develop precision-management procedures for FP8/FP4 training and inference in financial applications.
  • Profile and optimize workloads using NVIDIA Nsight Systems and Nsight Compute.
  • Build reusable GPU libraries and abstractions for modeling teams.
  • Evaluate and integrate RAPIDS, CUTLASS, cuBLAS, and TensorRT for financial use cases.

Requirements

  • BS or MS in Science, Technology, Engineering, or Math.
  • At least 1 year of experience required; 4–10 years preferred.
  • Expert-level CUDA programming, including kernel development, memory management, and stream and graph optimization.
  • Deep understanding of GPU architecture, including SM structure, warp scheduling, memory hierarchy, registers, shared memory, L1/L2, and HBM.
  • Experience profiling and optimizing GPU workloads.
  • Strong C++ and Python skills, with familiarity with mixed-precision computation and numerical stability.
  • Demonstrated ability to deliver meaningful speedups on real workloads rather than only benchmarks.

Nice to have

  • Background in HPC, scientific computing, or computational finance.
  • Experience with multi-GPU and multi-node GPU programming using NCCL or MPI.
  • Familiarity with GPU-accelerated data-processing frameworks such as RAPIDS and cuDF.

Culture & Benefits

  • Hybrid work policy with flexible in-office days and a home-office setup budget.
  • Fully paid medical and dental insurance for employees and dependents, plus employer-paid life and disability insurance.
  • Competitive 401(k) match, generous vacation, unlimited sick days, and paid caregiver leave.
  • Tuition reimbursement and sponsorship for conferences and training.
  • Onsite gyms with laundry service, wellness activities, casual dress, snacks, and game rooms.

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