Machine Learning Engineer for Quantitative Trading Systems

Citadel; City of Rochester (NY); Vor Ort; USD 275.000 - 350.000; Vollzeit

Benefits dieser Stelle

Medical insurance

Life insurance

Retirement plans

Tax-advantaged savings

Zusammenfassung

Global Quantitative Strategies (GQS), the quantitative investment unit of Citadel, seeks Machine Learning Engineers to design, build, and scale the models powering research and production workflows.

You will work embedded with researchers and engineers on deep learning, distributed training, and ML libraries, optimizing performance, reliability, and cost in a fast-paced, market-facing environment.

Qualifikationen

• Bachelor's, Master's, or PhD in Computer Science, Engineering, Mathematics, Statistics, Machine Learning, or an equivalent technical field.
• Strong programming skills in Python and experience with C++, CUDA, or other performance-oriented technologies.
• Experience designing, implementing, training, or optimizing machine learning models, particularly deep learning models.
• Strong understanding of model architecture, training dynamics, optimization techniques, and performance tradeoffs.

Aufgaben

• Design, implement, and optimize ML models and modeling systems powering research and production workflows.
• Collaborate closely with researchers and engineers to translate ideas into scalable architectures.
• Develop and improve ML tooling, libraries, and distributed training workflows.
• Improve training speed, inference performance, reliability, and cost efficiency across asset products.

Kenntnisse

Python

C++

CUDA

Deep learning

PyTorch

TensorFlow

Distributed training

Linux

Ausbildung

BSc/MSc/PhD

Tools

CUDA toolkit

MPI

Linux HPC

Jobbeschreibung

Global Quantitative Strategies (GQS), the quantitative investment unit of Citadel, seeks Machine Learning Engineers to design, build, and scale the models powering research and production workflows.

You will work embedded with researchers and engineers on deep learning, distributed training, and ML libraries, optimizing performance, reliability, and cost in a fast-paced, market-facing environment.