Learning JAX as a Senior ML Engineer: First Impressions and Mental Model

Short summary

A senior ML engineer shares first impressions of learning JAX after six years with PyTorch. JAX mirrors NumPy syntax but adds three key capabilities: automatic GPU/TPU placement, functional automatic differentiation via jax.grad, and JIT compilation via jax.jit. The biggest mental shift is JAX's immutability requirement — no in-place mutations — which enables pure functions that make grad and jit mathematically clean. The author explains why research labs like DeepMind build on JAX and Flax.

• •JAX mirrors NumPy syntax but adds automatic accelerator placement, functional grad, and JIT compilation
• •No in-place mutation is the biggest adjustment — every operation creates a new array, enabling pure functions for grad and jit
• •Research labs like DeepMind use JAX because compiled, composable, mathematically clean code scales across hundreds of TPUs

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