Machine Learning Engineer

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.