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.

Responsibilities:

• Research and explore new AI/ML methods through independent study, attending industry-leading conferences, and experimentation

• Develop state-of-the-art AI/ML models for real-world NLP, speech recognition, time-series forecasting, and recommendation systems. 

• Build and train production-grade AI/ML models on large-scale datasets to solve various business use cases.

• Use large-scale data processing frameworks such as Spark, AWS EMR for feature engineering and be proficient across various data both structured and unstructured.

• Use Deep Learning models like CNN, RNN, and NLP (BERT) for solving various business use cases like name entity resolution, forecasting, and anomaly detection.

• Ability to build ML models across Public and Private clouds including container-based Kubernetes environments.

• Develop end-to-end ML pipelines necessary to transform existing applications and business processes into true AI systems.

• Build both batch and real-time model prediction pipelines with existing application and front-end integrations.

• Develop large-scale data modeling experiments and extract key statistical insights and/or cause and effect relations.

Requirements:

• 5+ Years of Experience in Building & Deploying ML Models in Production for Healthcare, Finance, Insurance, Retail, Telecom, or Manufacturing Industries.

• MSc or PhD in Computer Science & Engineering or other relevant fields: Electronics Engineering, Industrial Engineering, Physics, Mathematics, etc.

• PhD-Level Research Experience in AI, ML, 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, etc). 

• Experience with ML techniques such as regression, classification, clustering, time series, econometrics, causal inference, mathematical optimization.

• Experience in designing, building, and deploying highly scalable distributed ML models in production using Python and PySpark.

• Experience in designing, building, and deploying end-to-end ML pipelines using DL frameworks like PyTorch and TensorFlow 2.0

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

• Experience in Nvidia Toolkits such as Nemo, Triton, TensorRT, Riva, etc.

Desired Skills:

• Kaggle Achievements and/or Open-Source Project Contributions.

• Excellence in Applied ML with statistical methods and algorithms

• Excellence in Deep Learning Frameworks: PyTorch, TensorFlow 2.0 

• Experience in Unsupervised, Semi-Supervised & Active Learning.

• Experience in Variational and Adversarial Generative DL Models.

• Experience in Autoregressive Time Series Forecasting Methods.

• Excellence in working with Tabular-Data via XGBoost & LightGBM

• Experience in Object Oriented Programming (Python, C/C++, Java) 

• Experience in MLOps and Cloud Platforms (AWS, Azure, and GCP)

• Experience in Hyperparameter Optimization Methods & Frameworks.

• Experience in Creating Back-End APIs and SQL/NoSQL Databases

• Experience with Data Analytics & Visualization (Tableau, Presto, AWS suite)

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

• Experience in Self-Supervised (Contrastive or non-Contrastive) Deep Learning.

• Experience in Neural Architecture Search, and Model Compression/Distillation

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