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TensorFlow is Google's open-source machine learning platform that provides a comprehensive ecosystem for building and deploying ML models across cloud, edge, and mobile devices. Its production-ready tooling, model serving infrastructure, and edge deployment capabilities make it a mainstay of enterprise ML. Companies hire TensorFlow developers for building production ML systems, deploying models with TensorFlow Serving, implementing on-device ML with TensorFlow Lite, and creating ML pipelines with TFX. TensorFlow developers work with Keras for model building, tf.data for data pipelines, TensorBoard for visualization, and integration with Google Cloud's ML infrastructure. The framework's ecosystem spans from research to production deployment. TensorFlow developer salaries range from $115k to $195k+ with production ML engineers and mobile ML specialists commanding premium compensation. While PyTorch has gained research popularity, TensorFlow's production ecosystem and edge deployment capabilities maintain its strong position. LeetHire connects TensorFlow developers with companies deploying ML at scale through anonymous hiring.
Yes, TensorFlow remains highly relevant, especially for production deployments. TensorFlow Serving, TensorFlow Lite for mobile/edge, and TFX for ML pipelines provide mature production tooling that PyTorch is still catching up to.
TensorFlow excels at production ML systems, mobile/edge deployment (TF Lite), ML pipelines (TFX), and large-scale training on TPUs. It's particularly strong for companies running ML in production at scale on Google Cloud.
TensorFlow developers earn $115k to $195k+ annually. ML engineers deploying models with TensorFlow Serving earn $130k-$180k+. Mobile ML specialists using TensorFlow Lite can earn $120k-$170k+.