JAX is Google's high-performance numerical computing library combining autograd with XLA compilation. It powers cutting-edge ML research at DeepMind and enables composable transformations like jit, grad, vmap, and pmap for accelerated model development.
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JAX is Google's open-source library for high-performance numerical computing and machine learning research. Think of it as NumPy on steroids: it takes familiar NumPy operations and supercharges them with automatic differentiation (autograd) and XLA (Accelerated Linear Algebra) compilation for GPUs and TPUs.
What makes JAX different from PyTorch or TensorFlow is its functional programming approach and composable transformations. The four core transformations — jit (just-in-time compilation), grad (automatic differentiation), vmap (automatic vectorization), and pmap (parallel mapping across devices) — can be composed arbitrarily. This gives researchers extraordinary flexibility to express complex mathematical operations efficiently.
JAX is the framework of choice at Google DeepMind. Projects like AlphaFold 2, Gemini, and numerous state-of-the-art research papers rely on JAX. It consistently outperforms PyTorch for certain workloads, particularly those involving large-scale parallelism across TPU pods and custom gradient computations.
That said, JAX is not a beginner-friendly framework. It has a steeper learning curve than PyTorch, a smaller ecosystem of pre-built models, and requires a solid understanding of functional programming concepts. If your team needs production-ready model serving with minimal friction, PyTorch or TensorFlow may be better choices. But if you're pushing the boundaries of ML research or need raw performance on custom architectures, JAX is hard to beat.
JAX developers are a niche talent pool. Most come from research backgrounds — PhDs or ML research engineers at top labs. This drives US salaries higher than general ML roles.
Latin America's top universities — USP in Brazil, UBA in Argentina, UNAM in Mexico — produce strong computational scientists who gravitate toward JAX for research work. Many have published in NeurIPS, ICML, or ICLR.
The time zone alignment with US teams is a major advantage for JAX roles specifically. ML research requires tight iteration loops — debugging gradient issues or tuning XLA compilation flags is much harder with an 8-hour time zone gap. LatAm developers work your hours.
The growing presence of Google's research initiatives in Latin America (including a DeepMind-adjacent office in Brazil) has expanded the local JAX talent pool significantly over the past two years.
South maintains a vetted network of ML engineers with specific JAX experience — not just PyTorch developers who've "looked at" JAX. Our screening process includes live coding assessments using JAX transformations, not generic Python tests.
We verify candidates' experience with the JAX ecosystem: Flax/Haiku for model building, Optax for optimization, and real distributed training experience on GPUs or TPUs. We also assess their ability to read and implement research papers, since that's what most JAX work involves.
Typical placement timeline is 2-3 weeks. We handle payroll, compliance, and equipment for your LatAm team members, so you can focus on research.
No. JAX and PyTorch serve different audiences. PyTorch dominates industry ML and has a much larger ecosystem. JAX excels in research settings, especially at Google/DeepMind. Many teams use both — JAX for research prototyping and PyTorch for production deployment.
Almost always yes. Developers who've gone deep on JAX typically have strong PyTorch backgrounds. The reverse isn't always true — JAX requires additional functional programming skills that not all PyTorch developers have.
Smaller than PyTorch or TensorFlow, but growing. We estimate several hundred senior-level JAX practitioners across the region, concentrated in Brazil, Argentina, and Mexico. Many are affiliated with university research labs or have industry experience at companies using Google Cloud TPUs.
Most clients hire JAX developers as full-time embedded team members for research projects lasting 6+ months. Contract engagements work well for specific paper implementations or model optimization sprints.
Not necessarily. JAX runs on CPUs and GPUs as well. However, JAX's pmap and its TPU integration are key advantages, so many projects benefit from Google Cloud TPU access. South can help coordinate hardware provisioning through your cloud provider.
