ML Infra/Systems Engineer

About the Role

Nuance Labs is building the next generation of emotionally expressive, real-time AI.

This is a critical role to build the infrastructure that powers our AI platform. You will own the systems that serve models at scale, orchestrate complex data workflows, and ensure our real-time video AI runs reliably with low latency for users worldwide.

Key Facts

* $10M seed round backed by Accel, South Park Commons, Lightspeed, and top angels including Synthesia’s former CPO.

* A world-class team of PhDs from MIT, UW, and Oxford with decades of industry experience at Apple and Meta, advancing real-time avatars from cutting-edge research to products used by millions.

* In-person collaboration, 5 days a week at Seattle HQ

Responsibilities

* Own Inference Infrastructure: Build and maintain the serving stack for multimodal AI workloads. Optimize for latency, throughput, and cost using batching strategies, autoscaling, and intelligent resource allocation.

* Real-Time Video Streaming: Architect systems to handle long-lived WebRTC connections with unpredictable client behavior, ensuring smooth video and audio delivery at scale.

* Orchestrate Data Workflows: Build robust pipelines for offline processing, evaluation, and training using orchestration frameworks like Dagster or Ray. Manage petabyte-scale video storage and network requirements.

* GPU Cluster Management: Configure and maintain GPU clusters using Kubernetes and Terraform. Implement monitoring, autoscaling based on custom metrics, and cost optimization strategies.

* Developer Tooling: Build CI/CD, evaluation, and versioning systems that enable safe, zero-downtime model deployments and rapid iteration cycles.

Requirements

* Infrastructure Expertise: Strong practical experience with Kubernetes, Terraform, and cloud platforms. You can design secure, scalable systems and debug complex distributed issues.

* Systems Programming: Proficiency in Python and experience with systems languages (Rust or Go). Comfortable profiling workloads and resolving compute, memory, or network bottlenecks.

* Orchestration & Pipelines: Experience managing large-scale offline workflows using tools like Dagster, Ray, Airflow, or similar frameworks.

* Production Operations: Deep understanding of production reliability, monitoring, incident response, and capacity planning for high-traffic services.

Preferred Experience

* Experience with WebRTC or real-time media pipelines in production

* Experience running GPU-backed inference services at scale (vLLM, Triton Inference Server, TensorRT)

* Knowledge of performance optimization and low-level systems debugging

* Familiarity with video/audio processing and storage systems

Application

To apply, email [email protected] with your CV and a short note on why your background is a great fit for this role.