Deployment Engineer

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 might be a fit if...

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

Sample projects include:

• -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

The Compression Company is building the next generation of compression: systems that radically reduce the cost of moving and storing high-value, high-volume data without destroying the information people rely on. We create codecs tailored to specific datasets and deployment environments, and ship them as production-ready encoder/decoder artifacts that run on real hardware - from edge devices to cloud GPUs. We're a small, highly technical team with high ownership and a strong bias toward shipping real systems. We care about measurable performance, strong engineering discipline, and building a platform that compounds with every deployment.

If it looks like there may be a fit, we'll start with a short conversation with our CEO. Next you'll have a technical discussion with our CTO focused on your prior work and how you approach hard engineering problems. Finally, we'll invite you onsite to work on a small project and spend time with the team.