We are building a team of industry and science leaders to achieve the vision of empowering innovation via state-of-the-art AI/ML for our customers. We are looking for Applied AI/ML Scientists & Engineers to help us create AI/ML products and solutions for various industries.
- At least an M.Sc. degree 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, and Time-Series Forecasting.
- Excellence in deep learning frameworks: PyTorch, TensorFlow 2.0, and Jax.
- Excellence in implementing Convolutional, Recurrent, Variational, Generative, and Transformer Architectures for text, image, speech, and time-series datasets.
- Experience in Natural Language Processing, Understanding or Generation for Machine-Translation, Question-Answering, and Virtual Agent (Chatbot) Systems.
- Experience in Unsupervised, Semi-Supervised, Self-Supervised, Robust, and Active Learning Methods for Deep Learning Models when few or noisy labels are available.
- Experience in working with Tabular-Data with FLAML, XGBoost, LightGBM, and SHAP.
- Experience in MLOps on Cloud Platforms (AWS, Azure & Google Cloud).
Experience in creating Back-End Software using SQL/NoSQL databases.
Experience in Back-End API Frameworks like Django, Flask, FastAPI, etc.
- 5+ years of research experience including 2+ years of industry experience.
- Open-source projects or contributions in GitHub, or Kaggle achievements.
- Publications in top-tier AI/ML conferences (such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA, ACL, EMNLP, ICASSP, Interspeech, AAAI, etc).
- Experience in building & deploying deep learning models in production for healthcare, finance, insurance, retail, telecom, manufacturing, etc.
- Experience in data-centric, interactive, and human-in-the-loop AI methods.
- Experience in variational, adversarial, and flows-based generative models.
- Experience in hyperparameter optimization libraries e.g., Ray-Tune, Katib, and NNI.
- Experience in AutoML, MLOps, and big-data tools and frameworks such as Kubeflow, MLflow, W&B, Hadoop, Spark, H2O, Kubernetes, Docker, KServe, etc.
- Experience in NVIDIA’s frameworks and toolkits e.g. TAO, Riva, Nemo, etc.
- Experience in building AI application prototypes with Streamlit framework.