Top Tech Jobs & Startup Jobs in Austin, TX

Lead design, development, and delivery of production-quality firmware for next-generation hardware. Architect firmware subsystems, implement C-based real-time and boot logic, perform low-level debugging, collaborate with silicon/hardware teams, improve CI/CD and validation frameworks, and mentor engineers.

The role involves creating and executing characterisation plans for silicon devices, managing tests, and reporting results in collaboration with engineering teams.

Lead end-to-end system validation for AI compute platforms, focusing on silicon bring-up, integration, and validation across hardware and software domains. Collaborate with engineering teams to deliver high-quality AI systems.

The role involves validating AI chips, leading test design in C/C++, debugging on FPGAs, automating tests in Python, and collaborating across teams.

The role involves creating and executing plans for the bring-up and characterisation of silicon devices, ensuring they operate correctly in various conditions. The engineer will collaborate with teams to develop automated testing systems, document requirements, and produce detailed reports on characterisation results.

Lead financial strategy and planning for a semiconductor and AI company, partnering with senior leadership on forecasting, performance management, and decision-support. Oversee financial models, scenario analysis, capital allocation, and team leadership to drive accountability and growth.

Lead and conduct post-silicon validation for AI compute blades and racks, ensuring performance meets product standards while collaborating with multiple teams and mentoring engineers.

The Electrical Engineer will design hardware systems for AI applications, develop schematics, create PCBs, and collaborate on designs through production.

The Staff AI Performance Engineer will optimize performance across ARM-based architectures and distributed systems, analyzing AI workloads and collaborating to enhance system efficiency.

Design and optimize storage architectures for AI data centers, focusing on NVMe SSDs and ensuring high-performance data flow to GPUs. Responsibilities include performance tuning, vendor engagement, and managing storage subsystems for AI workloads.