Sr. ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs

Sr. ML Kernel Performance Engineer, AWS Neuron, Annapurna Labs

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 Amazon’s custom machine learning accelerators, Inferentia and Trainium.

The Acceleration Kernel Library team is at the forefront of maximizing performance for AWS's custom ML accelerators. Working at the hardware-software boundary, our engineers craft high-performance kernels for ML functions, ensuring every FLOP counts in delivering optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration.

The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon’s Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch, enabling unparalleled ML inference and training performance.

As part of the broader Neuron Compiler organization, our team works across multiple technology layers—from frameworks and compilers to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of machine learning, high-performance computing, and distributed architectures.

• Design and implement high-performance compute kernels for ML operations, leveraging the Neuron architecture and programming models.
• Analyze and optimize kernel-level performance across multiple generations of Neuron hardware.
• Conduct detailed performance analysis using profiling tools to identify and resolve bottlenecks.
• Implement compiler optimizations such as fusion, sharding, tiling, and scheduling.
• Work directly with customers to enable and optimize their ML models on AWS accelerators.
• Collaborate across teams to develop innovative kernel optimization techniques.

• 5+ years of professional software development experience.
• 5+ years of programming with at least one software programming language.
• 5+ years of leading design or architecture of new and existing systems.
• 5+ years of full software development life cycle experience, including coding standards, code reviews, source control management, build processes, testing, and operations.
• Experience as a mentor, tech lead, or leading an engineering team.

• Bachelor's degree in computer science or equivalent.
• 6+ years of full software development experience.
• Expertise in accelerator architectures for ML or HPC such as GPUs, CPUs, FPGAs, or custom architectures.
• Experience with GPU kernel optimization and GPGPU computing such as CUDA, Triton, OpenCL, SYCL, or ROCm.
• Demonstrated experience with NVIDIA PTX and/or AMD GPU ISA.
• Experience developing high-performance libraries for HPC applications.
• Proficiency in low-level performance optimization for GPUs.
• Experience with LLVM/MLIR backend development for GPUs.
• Knowledge of ML frameworks (PyTorch, Tensor Flow) and their GPU backends.
• Experience with parallel programming and optimization techniques.
• Understanding of GPU memory hierarchies and optimization strategies.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully; and follow all federal, state, and local laws and Company policies.

The base salary range for this position is  -  USD annually. Final compensation will be determined based on experience, qualifications, and location. Additional benefits include health insurance, 401(k) matching, paid time off, and parental leave.