Performance Engineer, Astera Labs

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or a related technical field. We welcome both recent graduates with strong, directly relevant project, research, or internship experience and candidates with 2–5 years of industry experience in performance or systems engineering
• Hands-on experience running AI/ML workloads on GPU clusters — including benchmarking, performance analysis, and fine-tuning of workloads across clusters of GPUs or accelerators. This can come from industry, research, or substantial academic projects
• Demonstrated ability to debug and root-cause system-level performance issues across hardware, firmware, software, and network boundaries
• Excellent fundamental knowledge of compute algorithms, parallel algorithms, and AI/ML algorithms and workloads
• Strong working knowledge of computer systems, GPU systems, and datacenter networking — including PCIe and Ethernet fundamentals
• Working knowledge of GPU and CPU software stacks (e.g., CUDA, MPI, collective communication libraries, drivers, and OS-level performance tooling)
• Proficiency in scripting and automation (e.g., Python) to build test pipelines and analyze large performance datasets

Desirable

• In this role, you will define how the world measures scale-up fabric performance. You'll build the roofline models, benchmarks, and end-to-end workload studies that quantify our performance leadership, expose bottlenecks, and drive performance fine-tuning of real AI workloads on our fabric to inform product direction
• Your data will directly shape architecture, firmware, and product decisions — and fuel the marketing narrative that positions Astera Labs at the center of AI connectivity
• Performance Characterization & Benchmarking
• Establish theoretical and measured roofline models for Astera Labs' scale-up fabric across key performance metrics, defining the reference for all comparative testing
• Build and maintain baseline performance benchmarks using industry-standard tools such as NVBandwidth and NCCL across a range of GPU configurations and switch topologies
• Quantify the impact of differentiated Astera Labs AI fabric features (e.g., Hypercast, In-Network Computing) against baselines using both synthetic benchmarks and real inference workloads
• Real Workload Analysis & Fabric Scalability
• Run end-to-end inference model workloads on target hardware to capture real-world performance beyond synthetic benchmarks, supporting architecture decisions and customer-facing demonstrations
• Evaluate fabric performance as inference cluster size scales from 16 to 32 GPUs and beyond, identifying bottlenecks and building performance scaling models for state-of-the-art AI workloads
• Design and execute head-to-head performance comparisons against competing fabric switch solutions to produce data-driven differentiation evidence
• Design, build, and maintain automated lab infrastructure including test execution pipelines, traffic generation tooling, and data collection and reporting systems
• Enable repeatable, high-quality, and scalable performance measurements across all hardware configurations, reducing manual effort and accelerating the test cycle
• Share infrastructure and playbooks with the Product Applications team to accelerate customer application development and issue resolution
• Cross-Functional Impact & Innovation
• Partner closely with ASIC architecture, firmware, software, Product Definition, Product Applications, and Product Marketing teams to communicate findings, influence design decisions, and resolve performance-impacting issues
• Serve as a key technical resource in the early evaluation of new fabric architectures, interconnect technologies (UALink, PCIe Gen 6/Gen 7, Ethernet, UEC), and AI/ML communication paradigms
• Provide performance data, analysis, and live benchmark support for key customer engagements and industry events; produce clear, audience-appropriate performance reports, technical briefs, and marketing collateral, and maintain living documentation in Confluence