Senior Performance Engineer, Inference

About the role

We are hiring a Senior Performance Engineer to join our Product team. You are an expert on state-of-the-art inference performance and will serve as our resident expert on how Cerebras stacks up against alternative inference providers on both price and performance.

Responsibilities

• Design standardized benchmark suites for inference workloads (code generation, summarization, multi-turn conversation, agentic tool use) that enable fair, reproducible comparisons.
• Stay current with GPU optimization communities (CUDA, Triton, TensorRT) and evaluate how new kernel fusions, flash-attention variants, and quantization techniques shift performance ceilings.
• Build and continuously update a competitive pricing model covering token-based pricing, throughput-based pricing, and enterprise contract structures across major inference providers.
• Monitor industry announcements, pricing changes, and new product launches.
• Synthesize findings into actionable briefs for the Sales and Product teams.
• Partner with Sales to build deal-specific competitive analyses showing total cost of ownership and performance advantages for enterprise prospects.
• Collaborate with Product and Engineering to identify where competitors are closing gaps or where Cerebras has underappreciated advantages.
• Track third-party benchmarking sources (Artificial Analysis, InferenceX) and ensure Cerebras is well-represented and accurately measured.

Required Skills & Qualifications

• Deep practical experience with state-of-the-art open-source inference frameworks like vLLM, SGLang, or TensorRT-LLM.
• 5+ years of experience in ML systems, ML research engineering, or high-performance computing.
• Strong understanding of LLM inference economics: tokens, throughput, latency, batch sizes, precision trade-offs, and how these translate to customer cost.
• Strong understanding of transformer model architecture internals such as attention mechanisms (MHA, MQA,GQA, MLA, DSA, MHA) and KV-cache management, and how each affects memory and compute profiles.
• Self-directed and resourceful.

Preferred Background

• In ML research (publications or significant open-source contributions) with a systems or efficiency focus.
• Contributions to open-source inference or kernel optimization projects.
• Excellent communication skills.