AI Research Engineer, Inference
Hudson River Trading (HRT) is seeking an AI Research Engineer (Inference) 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 and “alternative” data (such as language) to make predictions about future market state.
As an inference research engineer on HAIL, you will have a general mandate to improve all aspects of large-scale model inference, including but not limited to GPU kernel development, novel inference devices like ASICs and FPGAs (both off-the-shelf and in-house), and data streaming. You will work closely with our researchers to co-design and improve our models, and shape the research agenda.
We have a complex inference solution involving a variety of devices deployed around the world to meet a variety of trading needs. We are simultaneously pursuing multiple strategies and developing many model types with different purposes, and we are strongly incentivized to squeeze as much as we can out of our systems. Your work will be directly, clearly, and highly impactful on the business, and it will be challenging: this is a field with no easy or obvious solutions.
Qualifications
- Strong engineering skills, especially any of: CUDA/Triton/Pallas/CuTe DSL kernel development, lower-level PyTorch/JAX/XLA development, CUDA Graphs, FPGA/ASIC experience
- Must have two or more years work experience building deep learning systems, for any domain: robotics, biology, chemistry, physics, audio, video, recommendations, etc.
- Experience translating methods between areas of application is highly valued
- LLM experience is valuable, but not necessary
- Finance experience is not required
The estimated base salary range for this position is 250,000 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. This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package.