Senior Applied AI/ML Engineer (Remote)
- Los Gatos, CA, USA
- Employees can work remotely
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.
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.