Overnight Building Performance Analyst Jobs in California

Senior Performance Engineer 

Location: San Jose, CA (On-site) 

Role Overview 

Astera Labs is a hyper-growth connectivity company enabling the rack-scale AI infrastructure powering the world's most advanced GPU clusters. Our Scorpio scale-up fabric switches are purpose-built to unlock the performance of next-generation AI workloads, and we're looking for a Senior Performance Engineer to demonstrate the real-world value of our silicon where it matters most: on real inference and training workloads running on GPUs at scale. 

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. 

Key Responsibilities 

• 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. 

• Test Infrastructure & Automation  

• 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. 

Basic Qualifications 

• 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. 

Preferred Qualifications 

• MS or PhD in Computer Engineering, Computer Science, Electrical Engineering, or a related field. 

• Experience with scale-up fabrics and next-generation interconnects such as UALink, PCIe Gen 6/Gen 7, Ethernet, or UEC. 

• Deep understanding of modern inference and training workloads (LLMs, MoE, recommender systems) and their communication patterns. 

• Experience developing roofline models and competitive performance analyses for switching, networking, or accelerator silicon. 

• Excellent written and verbal communication skills, with the ability to translate deep technical findings into concise executive summaries and customer-facing narratives. 

Salary range is $135,000 to $170,000 depending on experience, level, and business need. This role may be eligible for discretionary bonus, incentives and benefits.