Optiver - Austin, TX

posted 6 months ago

Full-time - Mid Level
Austin, TX
Securities, Commodity Contracts, and Other Financial Investments and Related Activities

About the position

Optiver is seeking a Machine Learning Performance Engineer to join our team, focusing on a pivotal AI initiative. This role offers the opportunity to have a significant impact across Machine Learning infrastructure, training, and inference challenges to advance our futures trading strategies. As a tech-driven trading firm and leading global market maker, Optiver is committed to improving the market by injecting liquidity, providing accurate pricing, increasing transparency, and acting as a stabilizing force in various market conditions. The Austin office, located in ‘The Domain' neighborhood, serves as the firm's innovation nucleus, emphasizing quantitative research, software, and hardware engineering initiatives. With a strong focus on tech innovation, the office is a hub for machine learning, research infrastructure, and big data computing, all while being situated in a city known for its vibrant music, food, and art scenes, as well as numerous outdoor activities. In this role, you will be responsible for building scalable and robust training and inference pipelines for deep learning. You will dive into the internals of open-source deep learning frameworks and enhance their functionality, identify and eliminate performance bottlenecks, and collaborate closely with researchers and other engineers. Additionally, you will develop an in-depth understanding of trading systems, which is crucial for the success of our initiatives.

Responsibilities

  • Build scalable and robust training and inference pipelines for deep learning
  • Dive into 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

Requirements

  • Expertise in internals of deep-learning frameworks like PyTorch, JAX, TensorFlow, etc.
  • Deep understanding of computer architecture
  • Experience in programming in C++ and Python

Nice-to-haves

  • Experience with JAX ecosystem (XLA, Flax, etc.)
  • Experience in programming for GPUs or other accelerators (CUDA, Triton, Pallas, etc.)
  • Linux system programming experience
  • Experience with large-scale distributed training
  • Contributions to open-source projects related to data science and machine learning

Benefits

  • 25 paid vacation days and market holidays
  • Fully paid health insurance
  • Daily breakfast and lunch
  • Training opportunities
  • 401(k) match up to 50%
  • Charitable match opportunities
  • Regular social events and clubs
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