Senior Machine Learning Engineer (Deep Learning)

• Design and build scalable training and inference systems for deep learning workloads.
• Optimize distributed training across GPU clusters using frameworks such as PyTorch and JAX.
• Develop low-latency inference infrastructure for production ML applications.
• Improve model serving, orchestration, monitoring, and deployment workflows.
• Partner closely with ML researchers and quantitative teams to accelerate experimentation and productionization.
• Drive performance improvements across compute, networking, storage, and model execution layers.

• 5+ years of experience in Machine Learning Engineering, AI Infrastructure, or Distributed Systems.
• Strong experience building production training and/or inference systems for deep learning models.
• Expertise with PyTorch, CUDA, distributed training, model serving, and GPU optimization.
• Experience with large-scale ML infrastructure, MLOps, Kubernetes, or cloud-native systems.
• Strong software engineering skills in Python and/or C++.
• Background in high-performance computing, low-latency systems, or large-scale distributed environments preferred.

Preferred Background

• Experience supporting LLMs, foundation models, reinforcement learning, or other large-scale deep learning workloads.
• Prior experience in quantitative finance, trading, or other performance-critical environments is a plus.

Our client is a leading quantitative trading firm seeking a Senior Machine Learning Engineer to build and scale the deep learning infrastructure powering next-generation trading and research systems. This role sits at the intersection of machine learning, distributed systems, and high-performance computing. You'll design and optimize large-scale training and inference platforms used by researchers and portfolio managers to develop, deploy, and operate state-of-the-art deep learning models in production trading environments.