Software Development Engineer (AWS ML), Machine Learning Israel (MLIL) — FLOW sub-team (Fleet Lifecycle & Operational Workflows)

Annapurna Labs designs silicon and software that accelerates innovation. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world. The MLIL FLOW team is looking for a Software Development Engineer to design and build automation, tooling, and monitoring systems for our next-generation ML accelerator servers. We build production software to validate, initialize, monitor, and qualify these servers — from first silicon through fleet-scale deployment. Our work spans hardware diagnostics, manufacturing test automation, CI/CD pipelines, operational dashboards, and data-driven fleet health monitoring. Key job responsibilities • Design and develop software infrastructure — automation frameworks, deployment systems, and test orchestration platforms that run at scale across manufacturing and production environments. • Work cross-functionally with Hardware, Manufacturing, and EC2 teams to automate coordinated software delivery and qualification workflows. • Debug and root-cause hardware/software interaction failures using systematic data analysis and automation-assisted triage. • Build and own CI/CD pipelines end-to-end: from code commit through build, test, deploy, and production validation — driving fast, reliable software delivery for hardware teams. • Create data pipelines and analytics systems (ETL, aggregation, real-time reporting) that transform raw hardware test results into actionable engineering insights. • Develop monitoring dashboards, alerting systems, and data visualization tools for fleet health, yield tracking, and performance benchmarking. • Own features end-to-end: from design through implementation, testing, deployment, and operational excellence. A day in the life You'll start your day reviewing telemetry data from overnight fleet validation runs, identifying patterns that could indicate hardware issues or test regressions. Your morning might involve developing a new dashboard feature that visualizes yield trends across manufacturing sites, or extending an automation framework to support a new test configuration. Afternoons often bring debugging sessions where you'll dig into a failing server — correlating sensor readings, firmware logs, and test results to triage whether it's a thermal issue, a faulty interconnect, or a software misconfiguration — then pairing with hardware engineers to translate your findings into an automated detection workflow.