Machine Learning Performance Engineer, Annapurna Labs

The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and generative AI workloads on Amazon's custom machine learning accelerators — Inferentia and Trainium. These chips power workloads for thousands of AWS customers, from large language model training runs to real-time inference serving billions of daily predictions.

We are building the first Neuron performance engineering team in Tel Aviv. As a Machine Learning Performance Engineer, you'll help shape the direction of this team from the ground up — profiling and optimizing workloads across the full ML software stack, writing high-performance kernels, and improving the Neuron SDK that external developers depend on. You'll work at the boundary between software and hardware, collaborating directly with compiler, runtime, and chip design engineers to close performance gaps customers care about.

The team is new and small, which means broad scope, direct ownership, and real influence over the technical direction we take. If you enjoy digging into performance bottlenecks and turning analysis into measurable wins, this role is for you.

Key job responsibilities
Design and implement high-performance compute kernels for ML operations, leveraging the Neuron architecture and programming models.
Profile ML workloads end-to-end to identify bottlenecks — memory, compute, or communication — and drive optimizations through to a measured improvement.
Enhance the programming model and tooling that kernel and model developers rely on, improving usability and debugging workflows.
Identify and drive optimization opportunities across the Neuron software stack (compiler, runtime, frameworks).
Document software designs, operational runbooks, and performance findings so the broader team can build on your work.

A day in the life
You might start your morning reviewing profiling data from a customer's large diffusion model training job, tracing a utilization gap back to a specific kernel. After a design discussion with compiler engineers about a new operator fusion strategy, you spend the afternoon writing and benchmarking a kernel prototype. Later, you review a teammate's pull request for a runtime optimization and share your findings in a short write-up for the broader Neuron organization. Your work directly translates into faster model execution and lower cost for AWS customers running ML workloads at scale.

About the team
The Neuron Performance Engineering team in Tel Aviv is part of Annapurna Labs within AWS. Our mission is to make sure every ML workload running on Inferentia and Trainium chips reaches its full performance potential. We partner closely with compiler, framework, and hardware teams across Annapurna Labs, and we work directly with AWS customers to understand their models and unblock their adoption.

We are a newly formed group which is part of the larger Neuron organization. If you want to shape a team's technical culture from its earliest days while working on problems that matter to the future of AI infrastructure, we'd love to hear from you.

Basic qualifications

- 3+ years of non-internship professional software development experience
- Knowledge of Python and/or C++ programming
- Knowledge of computer architecture, operating systems, and parallel computing
- Experience with PyTorch, TensorFlow, and/or JAX

Preferred qualifications

- Master's degree in Computer Science, Engineering, Mathematics, or a related field
- Experience optimizing performance for LLM, Vision, or other deep-learning models
- Experience with kernel writing or parallel programming (CUDA, Triton, CUTLASS, Pallas, Mojo, SIMD, MPI)
- Experience with compiler optimization or hardware-software co-design

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