About the RoleWe 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 at scale and have strong opinions about where they fall short.
This posting is aimed at experienced engineers and researchers who've operated at a senior to senior-staff level at big tech, a leading AI lab, or a high-traffic inference team. Everyone at Nuance is MTS - we don't run title ladders - but we're hiring people who have already done this work at scale.
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- Significant hands-on experience with LLM inference optimization - you've shipped work on KV caching, memory layout, attention kernels, or batching strategies in a production or high-traffic research context
- Proven proficiency with inference serving frameworks - vLLM, SGLang, TensorRT-LLM, or similar - including going well beyond default configurations and adapting them to non-standard workloads
- 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 a clear sense 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.
Logistics- Location: In-person in Seattle, five days a week - we believe in the compounding value of working shoulder-to-shoulder.
- Visa sponsorship: We sponsor visas (O-1, H-1B, green card) from day one.
- AI-native tooling: Do your best work with the best tools, including unlimited tokens.
Benefits- Health: HSA plan with ~$2,000 in annual company contributions - roughly 2x what most big tech companies put in.
- Time off: 15 days of PTO plus public holidays, and we close the office for a full week at year-end.
- Food: Lunch, drinks, and snacks on us every workday - the small thing that quietly makes the day better.
- Commuter benefits: We help cover the cost of getting to the office.
- 401(k): In the works.
