The Research Engineering team is dedicated to accelerating the velocity of machine learning research and expanding the exploration space for innovations at PDT. We partner with PDT's quantitative researchers to design and build a state-of-the-art environment for testing ideas rapidly and efficiently.
Research at PDT requires significant compute, and as such, we are looking for a talented engineer with in-depth knowledge of ML techniques and DL ecosystem to help us build the infrastructure capable of supporting complex scientific research at scale.
This is a hybrid position and will require the person to work from our New York City office at minimum 3 days a week.
Responsibilities: - Partner with the research team to understand future research directions and build the next generation of highly scalable infrastructure for alpha, signal, and portfolio construction.
- Incorporate advancements in machine learning, hardware accelerators and high-performance computing to optimize research workflows.
- Maintain, develop, and re-imagine the extensive internal research stack that continues to be a differentiating factor for PDT business.
- Optimize models for inference and use in real time trading systems.
Below is a list of skills and experiences we think are relevant. Even if you don't think you're a perfect match, we still encourage you to apply because we are committed to developing our people. - Experience with building infrastructure for training/fine-tuning large ML models.
- Intellectual curiosity and a strong interest in solving difficult problems.
- Exceptional programming skills and proficiency in identifying performance bottlenecks.
- Experience with the python scientific stack and DL libraries (PyTorch, Tensorflow, etc.)
- Experience with hardware accelerators.
- Previous experience in Quant Finance is not required.
The salary range for this role is between $190,000 and $250,000. This range is not inclusive of any potential bonus amounts. Factors that may impact the agreed upon salary within the range for a particular candidate include years of experience, level of education obtained, skill set, and other external factors.
