Senior Applied AI/ML Engineer (Remote)

  • Los Gatos, CA, USA
  • Employees can work remotely
  • Full-time

Company Description

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.

Job Description


  • 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.

Additional Information

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