Member of Technical Staff — Model Optimization and Inference

About the Role

We can train a great model. The next problem is making it fast enough to actually use in a real-time conversation — and that gap is enormous. A model that responds in 3 seconds is a demo. A model that responds in under 500ms is a product.

We're looking for someone who specializes in taking trained models and squeezing every last millisecond out of them. You understand the full stack from model weights to serving infrastructure — quantization, KV cache optimization, kernel-level acceleration, batching strategies — and you know which lever to pull for which problem. You've worked with vLLM, SGLang, or similar frameworks and have opinions about where they fall short.

Our stack is more complex than a standard LLM deployment: we're serving a full-duplex multimodal system that must satisfy strict real-time latency constraints. There's a lot of unsolved optimization work here, and we need someone who finds that genuinely exciting.

What You'll Do

• Own end-to-end inference optimization across our model stack — LLMs, audio models, and diffusion-based components
• Implement and tune KV cache strategies for long-context conversations, including eviction policies, compression, and memory-efficient attention
• Evaluate, deploy, and extend inference serving frameworks (vLLM, SGLang, TensorRT-LLM, etc.) for our specific workloads
• Profile and benchmark end-to-end latency and throughput; identify and systematically eliminate bottlenecks
• Build internal tooling that makes optimization work faster and more rigorous — profiling viewers, end-to-end inference test harnesses, and other infrastructure that helps the team move quickly
• Accelerate diffusion model inference — consistency models, step distillation, caching strategies, and custom kernel optimizations
• Apply and develop quantization techniques (INT8, INT4, GPTQ, AWQ, and beyond) to reduce memory footprint and increase throughput without meaningfully degrading quality
• Work closely with research and infrastructure to ensure new models ship with optimized serving from day one

What We're Looking For

• Deep expertise in LLM inference optimization — you've worked on KV caching, memory layout, attention kernels, or batching strategies in a production or research context
• Proficiency with inference serving frameworks — vLLM, SGLang, TensorRT-LLM, or similar — including the ability to go beyond default configurations and adapt them to non-standard use cases
• Experience optimizing diffusion model inference (latency reduction, step distillation, caching, or kernel-level work)
• Strong Python and PyTorch skills; comfort reading and writing CUDA or Triton kernels is a significant plus
• A systematic approach to profiling and optimization — you measure first, then optimize
• Familiarity with speculative decoding or other inference-time acceleration techniques

Bonus Points

• Hands-on experience with post-training quantization (GPTQ, AWQ, or similar) and understanding of quality/performance tradeoffs
• Familiarity with multimodal or streaming inference architectures
• Experience deploying real-time AI systems with hard latency SLAs
• Prior work at an AI lab, inference startup, or on a high-traffic model serving platform
• Contributions to open-source inference frameworks

Compensation

$250,000 – $350,000 base salary, plus meaningful equity. We think long-term ownership matters and structure equity accordingly.