Senior Distributed ML Training Systems Engineer

Amazon; Bellevue (WA); On-site; USD 168,000 - 227,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

RSUs

Paid time off

Parental leave

Job summary

Amazon in Bellevue, WA is seeking a Senior Software Development Engineer to design, build, and operate distributed services that run large-scale training and reinforcement learning workloads. You will own architectural direction and guide the team toward scalable solutions.

You will lead design and code reviews, mentor engineers, and partner with science, product, and partner teams to deliver scalable capabilities for model customization.

Qualifications

• 5+ years of professional software development experience.
• 5+ years of programming experience in at least one language.
• 5+ years of leading design or architecture of large systems.

Responsibilities

• Design, develop, and operate the distributed services that run large-scale training and reinforcement learning workloads.
• Build the scheduling, capacity, and fleet-health systems that place customer jobs onto large GPU clusters and keep them running through hardware failure.
• Build the infrastructure that hosts and scales the engines, including multi-tenant GPU sharing across customers.
• Partner with Science, Product, and partner service teams to translate evolving model and customer requirements into scalable technical solutions.
• Maintain a high operational excellence bar for services in the critical path of customer training workloads.

Skills

Non-internship software development

Programming experience in at least one

Leadership or architecture experience

Education

Bachelor's degree in computer science or equivalent

Job description

Amazon in Bellevue, WA is seeking a Senior Software Development Engineer to design, build, and operate distributed services that run large-scale training and reinforcement learning workloads. You will own architectural direction and guide the team toward scalable solutions.

You will lead design and code reviews, mentor engineers, and partner with science, product, and partner teams to deliver scalable capabilities for model customization.