Software Development Engineer, ML Systems Integration, Machine Learning Israel (MLIL) — Integration Validation
Amazon
Tel Aviv, Israel
Not specified; not specified; not specified
On-site
Ci/cd pipeline design and evolution
System-level test framework architecture
Firmware and data-plane validation
Amazon's Annapurna Labs is seeking a Senior Software Development Engineer for their ML Systems Integration team in Tel Aviv, Israel. The role focuses on designing and implementing CI/CD pipelines and test frameworks for next-generation ML inference accelerator servers in a dynamic, innovative environment
Job Summary
This role involves owning the end-to-end design and delivery of systems software for next-generation ML accelerator servers in a greenfield environment.
The successful candidate will architect system-level test suites to stress control-plane and data-plane components while building performance benchmarking infrastructure for LLM inference workloads.
You will leverage AI-assisted development tools to accelerate team velocity and pioneer new engineering workflows for continuous testing in production environments.
Matching Summary
Match Score: 85
Amazon's Annapurna Labs is seeking a Senior Software Development Engineer for their ML Systems Integration team in Tel Aviv, Israel. The role focuses on designing and implementing CI/CD pipelines and test frameworks for next-generation ML inference accelerator servers in a dynamic, innovative environment.
Salary
Not specified; Not specified; Not specified
Skills & Requirements
Must-have
CI/CD pipeline design and evolution
System-level test framework architecture
Firmware and data-plane validation
LLM inference workload benchmarking
Third-party vendor code integration
Nice-to-have
AI-assisted development tools usage
Experience with vLLM or NKI stacks
Production fleet readiness expertise
Cross-functional hardware collaboration
Key Requirements
Senior level independent technical leadership
Experience with firmware interfaces and data-plane performance
Proven track record in scaling validation infrastructure