Astera Labs: Purpose-Built Connectivity for Rack-Scale AI.
About the role
Astera Labs' Taurus product line includes Ethernet retimers and gearboxes deployed in active electrical cables and in-system applications at the heart of AI infrastructure. As AI clusters scale to tens of thousands of GPUs connected by high-speed Ethernet fabrics, the firmware running on these connectivity devices is mission-critical — and so is the ability to debug it fast when something breaks.
What they're looking for
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or related field
- 5+ years of experience in firmware development or embedded systems engineering
- Hands-on experience with Ethernet at the system or device level: Layer 1 PHY, SERDES, retimers, gearboxes, NICs, switches, or related devices
- Solid embedded C/C++ skills and comfort working in a firmware codebase on real hardware
- Ability to debug across the hardware/software boundary: register accesses, embedded SDKs, link state machines, PHY telemetry, debug print logs
- Familiarity with Linux development tools: gcc/clang, make, bash, gdb, git
More about this role
Astera Labs (NASDAQ: ALAB) provides rack-scale AI infrastructure through purpose-built connectivity solutions. By collaborating with hyperscalers and ecosystem partners, Astera Labs enables organizations to unlock the full potential of modern AI. Astera Labs’ Intelligent Connectivity Platform integrates CXL®, Ethernet, NVLink, PCIe®, and UALink™ semiconductor-based technologies with the company’s COSMOS software suite to unify diverse components into cohesive, flexible systems that deliver end-to-end scale-up, and scale-out connectivity. The company’s custom connectivity solutions business complements its standards-based portfolio, enabling customers to deploy tailored architectures to meet their unique infrastructure requirements. Discover more at www.asteralabs.com .
Astera Labs' Taurus product line includes Ethernet retimers and gearboxes deployed in active electrical cables and in-system applications at the heart of AI infrastructure. As AI clusters scale to tens of thousands of GPUs connected by high-speed Ethernet fabrics, the firmware running on these connectivity devices is mission-critical — and so is the ability to debug it fast when something breaks.
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