Who are you?
You have exceptional Machine Learning skills and have a knack for system design. Small theoretical experiments bore you out, inspiring you to test the limits of ML models in production. You like to play in the grey area between research and production, where answers are minted afresh rather than searched online.
You will be working with us and industry veterans across the world to deploy state-of-the-art
ML models on billions of devices. We are designing the ML systems of the future, the ones that are not sitting in a data center but running in real-time in the hands of the user.
• Design and develop the world’s first infrastructure for Machine Learning on the edge.
• Manage the complete lifecycle of ML models from designing the algorithms to deploying them over user devices.
• Participate in bleeding-edge research across federated learning, edge computing, and machine learning.
• Build libraries and frameworks for accelerating research in distributed machine learning. • Work closely with the core infrastructure and front-end team to deliver the ML models into production.
• Represent NimbleEdge in conferences and talks across the world.
• Worked extensively on designing & training ML models including deep neural networks. • Have a sound understanding of data modeling pipelines, optimization techniques, and prevalent ML architectures.
• Demonstrable experience with PyTorch or Tensorflow and tools used for data science like NumPy, Pandas, and W&B.
• Strong software development experience in Python, preferably open-source. Familiarity with Python/C++ interfaces like Cython is a plus.
• Explore new ML research for improving the performance of algorithms.
• Good understanding of Linux and cloud CLI (GCP, AWS, or Azure).
• Knowledge about JIT compilation and computational graph serialization is a plus.
• Exposure to popular MLOps frameworks like Kubeflow, Airflow, MLFlow, and DataRobot is a plus.
If you can communicate well and work methodically as part of a team, we’d like to meet you.