Senior Researcher - Graph Machine Learning

Key Skills

Aartificial intelligence, CNNs, Deep Learning, Effective communication skills, GPUs, Graph Learning, Large Language Models - LLMs, Machine learning techniques, NeurIPS, Python Programming, RNNs, TensorFlow, Transformer

Job Description

Our team has an immediate permanent opening for a Senior Researcher.

Responsibilities:

• Work on innovative ML models with applications on graph-structured data (such as Road understanding in autonomous driving, recommender systems, or AI for chip design support);
• Apply Graph Deep Learning techniques to large scale applications;
• Work on fundamental research for Graph Deep Learning (such as Scalable Graph Transformers, LLMs for Graphs etc.);
• Abstract and solve interesting research problem from internal application scenarios;
• Work closely with researchers and developers in the team;
• Write scientific reports, submit manuscripts to top-tier conferences and file high-value patents.

Job requirements

What you'll bring to the team:

• PhD degree in Computer Science, Statistics, Applied Mathematics or a related technical field or Master degree with 2+ years experience with industry-level deep learning models;
• Good knowledge of Deep Learning (DL) components (training, regularization, generalization, self-supervised learning) and familiar with common DL architectures (e.g., CNNs, RNNs, Transformers, LLMs);
• Have publication experience in top AI/ML conferences (e.g. NeurlPS, ICML, KDD, ICLR, KDD etc.);
• Knowledge in graph representation learning models (e.g., GNNs, Graph Transformers or Graph Generative Model);
• 4+ years of experience with mainstream ML frameworks (such as Tensorflow and Pytorch);
• 2+ year of experience in parallel model training on GPUs is an asset;
• 2+ years of end-to-end experience of training, evaluating, testing, and deploying industry-level models is an asset;
• Have good communication skills, eager to learn and willing to embrace new challenges for industry-level problem.