Inference Systems Performance Architect

The Inference Systems Performance team answers how fast SambaNova's systems can serve large language models and what it takes to get there. We capture real production traffic, benchmark it faithfully, model and simulate configurations that do not exist yet, and profile the distributed serving pipeline across host, accelerator, and fabric. Our work feeds serving optimization today and hardware and capacity planning for the next generation. We sit close to model optimization, systems, hardware, and product, and the whole company depends on our numbers.

You will be the architect for end-to-end inference performance: how a request moves through tokenization, prefill, decode, and the fabric between them, and how a deployment is sized against customer SLOs. The work has two coupled pillars. One is reproducible workload capture and benchmarking, building replayable representations of real and increasingly agentic traffic so measurements reflect production rather than a naive load script. The other is performance modeling and simulation, turning measurement into a what-if capability for configurations and hardware that do not exist yet.

The frontier you will help define is heterogeneous, disaggregated inference, with GPU on prefill and the RDU on decode. That opens hard problems in networking, storage, prompt caching, and tail-latency-bound data movement. Inference systems performance is a young field and most answers are still being discovered, so you will spend your time on ambiguous problems with no established solution, and you will set the technical direction that others build on.

• Define and drive the technical strategy for inference-systems performance including workload capture, benchmarking, modeling, and simulation, while developing and architecture that enables many potential futures
• Build the workload-capture and agentic-benchmarking capability - capture representative production traffic and enforce the discipline of interrogating results, spotting artificial contention or misleadingly high cache-hit rates that never occur in real use
• Own the performance-modeling and simulation practice - models that predict how a configuration change moves the output, informing capacity planning against customer SLOs and next-generation system and hardware planning
• Attack the end-to-end profiling gap - drive tooling that produces accurate, actionable profiles of a distributed inference pipeline so bottlenecks can be localized across host, accelerator, and fabric
• Serve as the senior technical voice across model-optimization, systems, hardware, and product, tying together multiple engineering activities and teams, and weighing trade-offs of reliability, scalability, operational cost, and ease of adoption
• Act as a resource for the entire organization including representing SambaNova's performance story to customers and partners
• Mentor and multiply by raising the capability of principal and senior engineers, building the systems, tools, and patterns that make everyone more productive
• Drive the resolution of the most ambiguous, novel challenges that span organizational boundaries or have no established answer in the field yet

• B.S. in Computer Science, Computer Engineering, or Related Field 
• 12+ years of experience in performance engineering, with a demonstrated record of technical leadership on large-scale, complex systems
• Deep expertise in end-to-end performance analysis of distributed systems with many moving parts and the ability to localize bottlenecks that others cannot
• Proven command of realistic workload generation and simulation and of performance modeling, including calibrating models against real, variable workloads
• Demonstrated ability to enter an unfamiliar domain and apply core performance methods with transferable discipline expertise 
• Ability to lead cross-functional efforts, mentor senior engineers, and influence organizational direction
• Experience representing an organizations credibly to customers and partners
• Track record of independently scoping and delivering high-complexity, high-ambiguity work with significant impact on products or roadmap

Preferred qualifications

• M.S. or PHD in Computer Science, Computer Engineering, or Related Field 
• Direct experience with LLM inference serving - continuous batching, prompt/KV caching, prefill/decode disaggregation, tail-latency SLOs
• Familiarity with inference simulation frameworks or agentic benchmarking efforts
• A public technical voice - talks, writing, or community presence on systems performance