AI Researcher

Requirements

• Must have two or more years work experience in deep learning research, for any domain: robotics, biology, chemistry, physics, audio, video, etc.
• Experience translating methods between areas of application is highly valued.
• LLM experience is valuable, but not necessary.
• Strong engineering skills valued, especially any of: GPU kernel development, lower-level Pytorch/JAX development, intermediate C++, FPGA/ASIC experience, HPC experience.
• The estimated base salary range for this position is 200,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience.
• Culture
• Hudson River Trading (HRT) brings a scientific approach to trading financial products. We have built one of the world's most sophisticated computing environments for research and development. Our researchers are at the forefront of innovation in the world of algorithmic trading.

Additional Information

Hudson River Trading (HRT) is hiring an AI Researcher to join the HAIL team. HAIL (HRT AI Labs) is the team at HRT responsible for developing and maintaining our most powerful models, which are used by our trading teams to drive a significant fraction of our trading. We are building and deploying "foundation models for markets", that ingest and train on vast amounts of market data, to make predictions about future market state. We are seeking experienced AI researchers to join our team to accelerate our efforts. Researchers have great independence to pursue the research directions they think would be most impactful as part of a small focused team with minimal bureaucracy. They are enabled by state-of-the-art research clusters with very high GPU-to-researcher ratios, and supported by excellent engineering, hardware, and systems teams to realize their vision. Your work will be directly, clearly, highly impactful on the business, and it will be challenging: this is a field with no easy or obvious solutions. You'll be responsible for improving every part of our models: from featurization of data, to architecture design, to training dynamics, to how trading decisions are made.

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