Machine Learning Systems Engineer (Inference) in New York

• 5+ years of experience in machine learning with a focus on training or inference systems \n

• Hands-on experience with real-time, low-latency ML pipelines in high-performance \n

• environments is a strong plus \n

• Strong engineering skills, including Python, CUDA, or C++ \n

• Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX \n

• Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT) \n

• Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL) \n

• Exposure to cloud platforms and orchestration tools \n

• A track record of contributing to open-source projects in machine learning, data science, or distributed systems is a plus \n

We are a global trading firm powered by a cutting-edge research environment and a world-class technology backbone. For decades, we’ve been a stabilizing force in financial markets, providing essential liquidity upon which market participants depend. Across our offices in the US, Europe, Asia Pacific, and India, our talented quant researchers, engineers, traders, and business operations professionals are united by our uniquely collaborative, high-performance culture, and our commitment to giving back. From entering dynamic new markets to embracing disruptive technologies, and from developing an innovative research environment to diversifying our trading strategies, we dare to continuously innovate and collaborate to succeed.

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As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and deployment of large-scale ML models across our global operations. You’ll collaborate with leading researchers, hardware experts, and software engineers to build robust solutions that maximize the potential of GPU acceleration, distributed computing, and the latest open-source tools. Your work will influence our trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement.

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This is a unique opportunity to solve problems at the intersection of advanced machine learning and trading, where your contributions will shape the future of our technology and trading capabilities.

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Your Core Responsibilities:

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• Develop large-scale distributed training pipelines to manage datasets and complex models \n

• Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems \n

• Develop libraries to improve the performance of machine learning frameworks \n

• Maximize performance in training and inference using GPU hardware and acceleration libraries \n

• Design scalable model frameworks capable of handling high-volume trading data and delivering realtime, high-accuracy predictions \n

• Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining \n

• Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs \n

• Evaluate and roll out third-party tools to enhance model development, training, and inference \n

• capabilities \n

• Dig into the internals of open-source ML tools to extend their capabilities and improve performance \n

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