Senior Machine Learning Engineer (MLOps)

About This Role

Design, build, and maintain end-to-end Machine Learning infrastructure to support and scale advanced research and trading models across the firm. This role involves optimizing GPU-based pipelines and partnering with ML Researchers to productionize models efficiently and reliably.

Responsibilities

• Design, build, and maintain end-to-end ML infrastructure for training, evaluation, and production inference
• Develop scalable systems for distributed training, model serving, and experiment management
• Optimize GPU-based pipelines for high-throughput and low-latency workloads
• Partner closely with ML Researchers to productionize models efficiently and reliably
• Build tooling for model lifecycle management: data ingestion, feature pipelines, versioning, monitoring, and retraining
• Ensure systems meet the latency, reliability, and scalability requirements of high-frequency trading

Requirements

• Strong experience building ML infrastructure or large-scale data/compute systems
• Deep knowledge of GPU systems, distributed computing, and performance optimization
• Hands-on experience supporting deep learning models in production at scale
• Expertise in Python and systems-level languages (e.g. C++, Rust, Go)
• Strong understanding of modern ML frameworks (e.g. PyTorch, JAX) from an infrastructure perspective
• Background from top-tier trading firms or large-scale tech companies
• Strong computer science fundamentals (systems, networking, concurrency)

Nice to Have

• Experience building low-latency or real-time systems
• Exposure to LLMs or large transformer-based models at scale
• Familiarity with Kubernetes, custom schedulers, or internal compute platforms
• Experience working alongside research-heavy ML teams

Skills

* Required skills