2 months ago
Montréal, CanadaSenior
Responsibilities
- Design and implement scalable, performant systems for serving machine learning models.
- Optimize deep learning training and inference performance, including deployment across different hardware configurations.
- Build and maintain feature pipelines, feature stores, model stores, production monitoring, and retraining infrastructure.
- Develop frameworks that support scalable and reproducible machine learning research.
- Drive projects, lead initiatives, and facilitate discussions on implementation, testing, and software architecture best practices.
- Collaborate with team members and stakeholders across departments to resolve challenging design and coding issues.
Requirements
- Bachelor’s or master’s degree in computer science, software engineering, or equivalent.
- At least 5 years of experience building software for machine learning systems.
- Strong understanding of machine learning and deep learning concepts, with experience building, validating, deploying, monitoring, and updating production models.
- Experience with infrastructure and tooling for MLOps.
- Strong programming, scripting, problem-solving, analytical, communication, and collaboration skills.
- Experience with TensorFlow, PyTorch, TensorRT, and ONNX.
- Experience with GPU programming using CUDA, OpenCL, or similar libraries.
- Experience optimizing inference performance through quantization, profiling, kernel or runtime tuning, and serving engines.
- Experience with distributed systems, multi-threaded programming, or hardware/software interaction is an asset.
Tech Stack
PyTorchTensorFlow
Categories
About DRW
DRW is a privately held proprietary trading firm that uses its own capital to trade and invest across global markets and asset classes, powered by technology and research. Founded in 1992 and headquartered in Chicago, it develops trading systems for strategies ranging from seconds to years and manages its own risk rather than external client money. The firm operates internationally from multiple offices.