Quincus

Quincus

Machine Learning Engineer

Toronto

Sponsorship not specifiedDetected 1187 days ago
PythonJavaC++Distributed SystemsAlgorithmsGCPDockerKubernetesMachine LearningDeep LearningTensorFlowSupply ChainLogisticsElectrical EngineeringResearchCommunicationCollaboration

About the role

  • "Make every logistics journey your best one yet" The Company.
  • We solve today's global supply chain challenges with groundbreaking technology.
  • Savoir, Chief Executive Officer, appeared on Forbes' 30 Under 30 Asia List in 2020, and also serves on the boards of several startups.

Requirements

  • 3+ years of experience in software engineering or machine learning engineering.
  • Experience with deep learning frameworks such as TensorFlow or PyTorch.

Nice to have

  • Ph.D. in Computer Science, Electrical Engineering, or a related field.
  • Strong understanding of computer architecture and performance optimization.
  • Strong communication and collaboration skills.
  • Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field..
  • Strong programming skills in Python (C++ or Java a plus).
  • Experience with GPU programming using CUDA, OpenCL, or similar libraries..
  • Experience with distributed systems and cloud computing platforms such as Kubernetes, Docker, GCP, and AWS.

Benefits

  • Design and implement scalable systems for serving deep learning and reinforcement learning models.
  • Optimize inference performance of deep learning and reinforcement learning models using techniques such as quantization, pruning, and distillation.
  • Develop and deploy production workflows for training and serving machine learning models.
  • Collaborate with data scientists and software engineers to design and implement machine learning systems.
  • Monitor and improve the performance of machine learning models in production.
  • Stay up-to-date with the latest research and techniques in deep learning and reinforcement learning.
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This listing is sourced directly from Quincus's careers page and normalized into a canonical job model.