PDT Partners
Performance Engineer
New York, NY
Sponsorship not specifiedDetected 97 days ago
PythonLinuxPyTorch
About the role
- Performance Engineers at PDT are responsible for deeply understanding and optimizing the systems that enable our trading strategies at scale.
- Your work directly amplifies researcher velocity and scales our core models, creating massive impact through both cost savings and accelerated innovation.
- This is a hybrid position and will require the person to work from our New York City office at a minimum of 3 days a week.
Responsibilities
- Focus on infrastructure/system-level efficiency, working across Python, PyTorch, OS, networking, storage, and CPU/GPU layers to optimize compute resource utilization
- Read and understand software layers, providing suggestions/PRs that optimize parts of codebases.
- Support scaling of core models by ensuring efficient implementation
- Propose and implement systems to improve performance telemetry
- Conduct proof-of-concept (PoC) evaluations and contribute to system design
- PDT Partners has a stellar 30+ year track record and a reputation for excellence.
Requirements
- Strong proficiency in Linux and its associated performance engineering toolset.
- Experience with PyTorch, GPUs and CUDA for optimization.
Compensation
- The salary range for this role is between $195,000 and $225,000.
- 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.
Benefits
- This range is not inclusive of any potential bonus amounts.
Company info
- 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.
- 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.
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This listing is sourced directly from PDT Partners's careers page and normalized into a canonical job model.