Deployment Engineer

Job Overview

The role:

You will own how our codecs run in real systems. You'll take trained models and turn them into fast, reliable encoder/decoder artifacts across edge devices and cloud GPUs. You'll build and maintain our compilation and release pipeline, improve runtime performance, and make deployment predictable and repeatable. You'll also own the cloud-side infrastructure that supports this workflow (build, artifact storage, versioning, and reproducible environments).

This role is central to shipping production integrations and scaling deployments across customers.

• You have shipped ML inference systems into production.
• You have worked with GPU runtimes (TensorRT, CUDA, ONNX Runtime, or similar).
• You can profile and optimise latency, memory, and throughput.
• You are comfortable owning AWS-based infrastructure for builds, artifacts, and delivery workflows.

• Hardening ONNX → TensorRT export and compilation for repeatable builds.
• Profiling and optimising encoder/decoder performance on constrained hardware.
• Building a clean "model → artifact → release" pipeline with strong versioning and provenance.
• Maintaining the AWS infrastructure that supports builds, artifact registries, and customer delivery.

Salary: $180k–$240k;
Equity:
Founding equity with meaningful ownership;
Full relocation & visa support.