Job description

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 e cosystem 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. Why join us?  PDT Partners has a stellar 30+ year track record and a reputation for excellence . Our goal is to be the best quantitative investment manager in the world . PDT’s exceptional e mployee-retention rate speaks for itself . Our people are intellectually curious, collaborative , down-to-earth, and diverse. 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 p ython 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. PRIVACY STATEMENT: For information on ways PDT may collect, use, and process your personal information, please see PDT’s privacy notices .

Requirements

Must-haves

This is a hybrid position and will require the person to work from our New York City office at minimum 3 days a week.
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 p ython scientific stack  and DL libraries ( PyTorch , Tensorflow , etc.)
Experience with hardware accelerators .
Previous experience in Quant Finance is not required .
PRIVACY STATEMENT: For information on ways PDT may collect, use, and process your personal information, please see PDT’s privacy notices .

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningml

Platform: greenhouse
Effort: quick