Member of Technical Staff - Model Optimization and Inference
Transparent Search Group · Seattle, Washington, United States
Member of Technical Staff - Model Optimization and Inference
Company: Nuance Labs
Location: Seattle, WA (in office 5 days per week; relocation assistance available)
Compensation: $250,000 - $350,000 + equity
Employment Type: Full-time
Visa Sponsorship: Visa sponsorship available
About Nuance Labs
Nuance Labs builds photorealistic, real-time AI avatars with emotional intelligence: a full-duplex audiovisual system that can listen, speak, react and respond like a real person. The research team includes PhDs from MIT, UW, Oxford, CMU and Johns Hopkins.
Founded in 2024, Nuance Labs has raised $60M and has about 25 people.
The Role
Nuance Labs is hiring an experienced ML Infrastructure/Systems Engineer (2+ years) to own end-to-end inference optimization across LLMs, audio models and diffusion components, with a focus on latency, throughput and cost.
What You Will Do
- Own end-to-end inference optimization across the model stack.
- Implement and tune KV cache strategies for long-context conversations.
- Evaluate, deploy and extend serving frameworks such as vLLM, SGLang and TensorRT-LLM.
- Profile and benchmark latency and throughput and remove bottlenecks.
- Accelerate diffusion inference and apply quantization techniques (INT8, INT4, GPTQ, AWQ).
- Build internal tooling that makes optimization work faster and more rigorous.
What You Bring
- 2+ years building and maintaining production ML systems
- Designing scalable infrastructure from scratch
- Track record optimizing latency, throughput and cost
- Debugging distributed systems
Nice to Have
- Video or audio model experience
- CUDA kernels and low-level optimization
- Real-time video streaming (WebRTC)
Benefits
HSA with about $2,000 annual company contribution, 15 days PTO plus public holidays and a company-wide office closure week.
Tech Stack
Kubernetes, Terraform, Python, Rust, Go, Dagster, Ray, Airflow, WebRTC, vLLM, Triton Inference Server, TensorRT