Top Tech Jobs & Startup Jobs

Drive automation-based validation execution for AI hardware platforms, defining validation strategies and ensuring system readiness across blade and rack levels.

Lead and build an SLT test engineering team to develop system-level test capabilities for multi-die AI processors. Define manufacturing test solutions, design test fixtures, write production and characterization test programs on SLT platforms, collaborate across silicon, test, DFT and manufacturing teams, transfer solutions to volume manufacturers/OSATs, and support bring-up, failure analysis and yield improvement.

Lead power, thermal and performance validation for AI compute silicon and systems across pre- and post-silicon phases. Define validation strategies, develop automation and benchmarks (Python), run post-silicon bring-up, characterize CPU/memory/interconnect/I/O under workloads, analyze data to find bottlenecks, and collaborate with cross-functional teams to remediate and improve validation infrastructure.

Lead and execute power, thermal and performance validation for AI compute silicon and systems across pre- and post-silicon stages. Define validation strategies, develop automation and benchmarks (Python), run characterization, analyze large datasets to find bottlenecks, and collaborate with architecture, RTL, firmware and systems teams to drive performance and reliability improvements.

Lead architecture and implementation of system management interfaces for AI infrastructure. Translate strategy into technical plans, mentor engineers, improve reliability, automate deployment and CI/CD, collaborate across hardware, firmware and platform teams, and drive production readiness and observability.

Lead strategic sourcing for memory and storage technologies (DRAM, SSD, HDD) supporting AI infrastructure. Develop supplier strategies, manage relationships, negotiate commercial agreements, ensure capacity and supply continuity, perform cost modelling and market intelligence, drive supplier performance and risk mitigation, and support new product introductions with cross-functional teams.

The Principal Network Engineer oversees network fabric maintenance for AI datacenters, collaborates on high-speed network design, and troubleshoots complex routing protocols.

Lead end-to-end strategic sourcing for AI infrastructure commodities: supplier selection, negotiation, cost modeling, performance management, risk mitigation, and cross-functional collaboration to ensure resilient supply for Graphcore's compute platforms.

Lead the end-to-end manufacturing test strategy from board (L6) to rack (L11), manage a 3-5 engineer team, partner with hardware/firmware/validation/quality/JDM, define test infrastructure and capacity, drive yield improvements, CAPA, and continuous manufacturing quality improvements.

Lead end-to-end signal integrity analysis and optimization for blade- and rack-level high-speed compute platforms. Perform SI/PI simulations and hands-on lab validation (VNA, TDR, BERT, oscilloscopes), correlate measurements with simulations, debug margin gaps, and collaborate with silicon, package, board, connector, cable, ODMs and vendors to improve I/O channel performance and produce technical validation reports.