Our client is a B2B supply chain operating SaaS platform headquartered in Singapore and build their Research team in Canada. They solve today's global supply chain challenges with groundbreaking technology. Using AI and machine learning, they have digitized and optimized the logistics process while giving customers full transparency into their supply chain.
As a Machine Learning Engineer you will develop and deploy deep learning and reinforcement learning algorithms at scale. As a machine learning engineer, you will be responsible for designing and implementing scalable systems for serving models, optimizing inference performance, and managing production workflows.
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.
Utilize GPU computing to accelerate model training and inference.
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.
Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
3+ years of experience in software engineering or machine learning engineering.
Strong programming skills in Python (C++ or Java a plus)
Experience with deep learning frameworks such as TensorFlow or PyTorch.
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.
Ph.D. in Computer Science, Electrical Engineering, or a related field.
5+ years of experience in software engineering or machine learning engineering.
Experience with reinforcement learning algorithms and frameworks.
Experience with production deployment of machine learning models and implementation of APIs for big data.
Strong understanding of computer architecture and performance optimization.