Engineering Manager: ML Network Stack & Systems

Amazon; Seattle (WA); On-site; USD 185,000 - 250,000; Full time

Job summary

Annapurna Labs, part of AWS, is seeking a Software Development Manager to lead the team responsible for the ML network stack for EC2 distributed AI/ML and HPC systems. You will guide engineers in systems programming, networks, RDMA, HPC, and HW/SW co-design to deliver features for cutting-edge ML workloads.

The role requires strong leadership across product/PM interfaces and hands-on architectural experience, with a focus on scalable, reliable systems across the ML ecosystem.

Qualifications

• 3+ years of engineering team management experience.
• 5+ years of working directly within engineering teams experience.
• 3+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience.
• 5+ years of leading the definition and development of multi tier web services experience.
• Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations.
• Experience partnering with product or program management teams.

Responsibilities

• Direct teams to deliver functions and features for the latest and largest ML workloads.
• Ensure coordination across hardware/software/network components for ML/EC2 workloads.

Skills

Engineering management

Team collaboration

System design & architecture

Multi-tier web services

Software/hardware life cycle

Product management partnership

Job description

Annapurna Labs, part of AWS, is seeking a Software Development Manager to lead the team responsible for the ML network stack for EC2 distributed AI/ML and HPC systems. You will guide engineers in systems programming, networks, RDMA, HPC, and HW/SW co-design to deliver features for cutting-edge ML workloads.

The role requires strong leadership across product/PM interfaces and hands-on architectural experience, with a focus on scalable, reliable systems across the ML ecosystem.