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


  • 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.



  • 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

Additional Information

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