The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on AWS Trainium, Amazon's custom machine learning accelerator. Neuron includes an ML compiler, runtime, collectives library, and application framework that integrate with PyTorch and JAX, so customers can train frontier-scale models on Trainium without rewriting their stack.

The Distributed Training team enables the training of a wide range of models, from large-scale pretraining through post-training and reinforcement learning, on AWS's custom ML accelerators. As more customer workloads shift toward RLHF, PPO/GRPO, and other fine-tuning methods, we are building the distributed training infrastructure, parallelism techniques, numerics, and high-performance kernels that these methods depend on. As part of the broader Neuron organization, we work across frameworks, kernels, compiler, runtime, and collectives - a true hardware and software co-design in practice. We not only optimize current performance but also contribute to future architecture designs, since the gaps we characterize today become requirements for the next generation of Trainium.

We are looking for passionate technical leaders who can help us build and fine tune these distributed training solutions. This role offers a rare opportunity to work at the intersection of machine learning, high-performance computing, and distributed systems, where you will help shape the direction of AI acceleration technology.

Key job responsibilities

You will lead the effort to build distributed training and post-training support into PyTorch and JAX for Trainium accelerators. You will work across PyTorch and Neuron software stack with the Neuron compiler and runtime teams to enable and fine tune large-scale training, post training, and reinforcement learning workloads on the latest Trainium instances. You will own the parallelism strategies these models depend on, spanning data, tensor, pipeline, expert, and context parallelism, and apply reduced-precision formats where they measurably pay off. You will profile end to end to determine whether a workload is bound by compute, memory, collectives, or host overhead, then drive the fix to the layer that owns it, working with compiler, runtime, and collectives engineers to land it. You will translate the performance gaps you characterize into requirements that influence frameworks, and contribute upstream to the open source frameworks our customers train on.

BASIC QUALIFICATIONS

- 5+ years of non-internship professional software development experience

- 5+ years of programming with at least one software programming language experience

- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience

- Experience as a mentor, tech lead or leading an engineering team

- Bachelor's degree or above in computer science or equivalent

- * Familiarity with LLM/transformer fundamentals such as attention mechanisms, autoregressive decoding, V-cache behavior, forms of parallelism

- * Experience in machine learning, data mining, information retrieval, statistics or natural language processing

PREFERRED QUALIFICATIONS

- Master's degree or above in computer science or equivalent

- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience in computer architecture

- * Hands-on SFT and/or RLHF/PPO/DPO experience

- * Experience with ML frameworks such as Pytorch/Jax, Distributed libraries and Frameworks, RL frameworks or End-to-end Model Training

- * Experience with performance engineering: workload profiling, characterization (compute bound, memory bound, network bound), and optimization

- * Contribution to open source projects

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, CA, Cupertino - 193,300.00 - 261,500.00 USD annually