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 Machine Learning Engineering interns to help us create AI/ML products and solutions for various industries.

This opportunity is open to international students located in Brazil, Turkey, Egypt, India, Saudi Arabia, Qatar, Senegal, Germany and the UK

Requirements:

• At least B.Sc. degree in Computer Science & Engineering or other relevant fields such as Electronics Engineering, Industrial Engineering, Physics, Mathematics, etc.

• At least 1 year of previous working/intern xxperience in AI, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Speech Recognition, 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, 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.

Desired Skills:

• 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, 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.