Engineering Manager, ML Kernel Performance

Amazon Web Services (AWS); Cupertino (CA); On-site; USD 213,000 - 288,000; Full time

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Paid time off

Parental leave

Job summary

Annapurna Labs (U.S.) Inc. in Cupertino, CA is seeking a senior kernel engineer to design and optimize high-performance ML compute kernels for AWS accelerators. You will work across compiler, runtime, and hardware teams to accelerate ML workloads and improve performance.

You will analyze bottlenecks, apply advanced kernel optimizations, and collaborate with customers to enable their models on Neuron hardware. This role combines software, hardware, and ML systems in a startup-like environment.

Qualifications

• 3+ years of engineering team management experience.
• 7+ years of working directly within engineering teams experience.
• 3+ years of designing or architecting multi-tier web services.
• 8+ years of leading the definition and development of multi tier web services.

Responsibilities

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

Skills

Engineering management

Engineering teams

Low-level optimization

System architecture

ML model acceleration

Profiling tools

Job description

Annapurna Labs (U.S.) Inc. in Cupertino, CA is seeking a senior kernel engineer to design and optimize high-performance ML compute kernels for AWS accelerators. You will work across compiler, runtime, and hardware teams to accelerate ML workloads and improve performance.

You will analyze bottlenecks, apply advanced kernel optimizations, and collaborate with customers to enable their models on Neuron hardware. This role combines software, hardware, and ML systems in a startup-like environment.