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Applied AI/ML Engineer (Remote)

Los Gatos, CA, United States
aiXplain
ago
remote fulltime ASR NLP

We are building a team of industry and science leaders to achieve the vision of empowering innovation via state-of-the-art AI/ML research for our customers. We are looking for Applied AI/ML Scientists & Engineers to help us create AI/ML products & solutions for problems across many different industries.

Requirements:

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

  • M.Sc.-Level Research Experience in AI, Machine-Learning, Deep Learning, Computer Vision, Natural Language Processing, Speech Recognition, Time-Series Forecasting, 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 & Google Cloud).

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

Desired Skills:

  • 2+ 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 AutoML, MLOps, and Big-Data Tools and Frameworks such as Kubeflow, MLflow, Hadoop, Spark, H2O, Kubernetes, Docker, etc.

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

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

  • Knowledge in Self-Supervised (Contrastive) Deep Learning Techniques.

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

  • Interest in Neural Architecture Search, Model Compression & Distillation.

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