About The Position

Optiver is seeking a Machine Learning Performance Engineer to focus on a pivotal AI initiative. The role offers the opportunity to make a significant impact across machine learning infrastructure, training, and inference to advance futures trading strategies. The engineer will build scalable and robust deep-learning training and inference pipelines, work within open-source deep-learning frameworks to enhance their functionality, identify and eliminate performance bottlenecks, collaborate with researchers and engineers, and develop an in-depth understanding of trading systems.

Requirements

  • Strong knowledge of low-level GPU programming with CUDA, including Tensor Cores, cooperative groups, graphs, and warp-level intrinsics.
  • Expertise in the internals of deep-learning frameworks such as PyTorch, JAX, and TensorFlow.
  • Deep understanding of computer architecture.
  • Experience with C++ and Python.

Nice To Haves

  • Experience with the JAX ecosystem, including XLA and Flax.
  • Familiarity with GPU libraries and tools such as Triton, CUB, cuDNN, and cuBLAS.
  • Linux system programming experience.
  • Experience with large-scale distributed training.
  • Contributions to open-source projects related to data science and machine learning.

Responsibilities

  • Build scalable and robust training and inference pipelines for deep learning.
  • Explore the internals of open-source deep-learning frameworks and enhance their functionality.
  • Identify and eliminate performance bottlenecks.
  • Collaborate closely with researchers and other engineers.
  • Develop an in-depth understanding of trading systems.

Benefits

  • Highly competitive compensation package.
  • Global profit-sharing pool and performance-based bonus structure.
  • 401(k) match up to 50%.
  • Comprehensive health, mental, dental, vision, disability, and life coverage.
  • 25 paid vacation days alongside market holidays.
  • Office perks including breakfast, lunch, snacks, regular social events, clubs, and sporting leagues.
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