aiXplain, Inc.

Come join a team of industry and science leaders to achieve a vision of empowering innovation through state-of-the-art artificial intelligence leveraging multiple cloud technologies at aiXplain, Inc. We are addressing exciting challenges for our customers, at the intersection of AI/ML and cutting-edge cloud infrastructure.

Requirements:

• M.Sc. in Computer Science & Engineering or other relevant fields such as electronics Engineering, Industrial Engineering, Physics, Mathematics, etc.

• PhD-Level Research Experience in AI, Machine-Learning, Deep Learning, Computer Vision, Natural Language Processing, Speech Recognition, etc.

• Publications in Top-Tier AI/ML Conferences (such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA, ACL, EMNLP, ICASSP, AAAI, SIGGRAPH, etc). 

• Open-Source Projects or Contributions in Github. Kaggle Achievements.

• Excellence in Deep Learning Frameworks: PyTorch, TensorFlow 2.0, Trax. 

• Excellence in Building Convolutional, Recurrent, Variational, Generative, and Transformer Architectures for both Image and Time-Series Datasets.

• Experience in Natural Language Processing, Understanding or Generation for Machine-Translation, Question-Answering, and Virtual Agent Systems.

• Experience in Unsupervised, Semi-Supervised & Active Learning Methods for training Deep Learning Models when few or noisy labels are available.

• Excellence in working with Tabular-Data using XGBoost, LightGBM, etc.

• Experience in Object Oriented Programming via Python, C/C++, and Java. 

• Experience in MLOps on Cloud Platforms (AWS, Azure, and Google Cloud).

• Experience in Creating Back-End Software using SQL/NoSQL Databases.

• 5+ years of industry experience.

• Experience in Building & Deploying Deep Learning Models in Production for Healthcare, Finance, Insurance, Retail, Telecom, Manufacturing, etc.

• Experience in Variational, Adversarial & Flows-based Generative Models.

• Experience in Hyperparameter Optimization Frameworks e.g. Ray Tune.

• Experience in MLOps and Big-Data Platforms such as Kubeflow, MLflow, Hadoop, Spark, H2O, Kubernetes, Docker, etc.

• Experience in Autoregressive Time Series Forecasting (Facebook Prophet).

• Knowledge in Probabilistic Neural Networks (via GPyTorch, BoTorch, etc).

• Knowledge in (Deep) Reinforcement Learning or Graph Neural Networks.

• Knowledge in Self-Supervised and Contrastive Deep Learning Techniques.

• Knowledge of Gradient-based and Gradient-free Optimization Methods.

• Knowledge in Neural Architecture Search. Model Compression/Distillation.

• Knowledge of AutoML Tools and Frameworks e.g. for Feature Generation.

All your information will be kept confidential according to EEO guidelines.