Machine Learning Engineer
Hang- Full Time
- Senior (5 to 8 years)
Candidates must possess strong programming skills, including proficiency in languages such as Python, Java, or C++. They should have a proven track record of training and deploying large-scale machine learning models, particularly deep neural networks (DNNs), with a good understanding of distributed computing for learning and inference. Familiarity with cloud platforms like GCP, AWS, or Azure, along with ML serving solutions like Ray, Kubeflow, or Weights & Biases, is required. A deep understanding of DNN architectures, experience building, debugging, and fine-tuning models, and knowledge of frameworks like PyTorch or TensorFlow, along with familiarity with techniques such as knowledge distillation and recommender systems, are also necessary. Dev-Ops skills, including the ability to establish and manage data and compute infrastructure like Kubernetes and Terraform, and data engineering knowledge encompassing data pipelines and processing, are essential.
The Machine Learning Engineer will own and contribute to foundational models that power the recommendation pipelines, contribute to the research and development of recommender models while experimenting with the latest ML innovations, and design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering impact to daters. They will collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process, mentor and educate ML Engineers on current and up-and-coming research, technologies, and best practices, and perform other job-related duties as assigned.
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