Senior AI Infrastructure Engineer (Zürich, 100%)

Loki Robotics; Zürich; Vor Ort; CHF 140.000 - 210.000; Vollzeit

Zusammenfassung

Loki Robotics is hiring an AI Infrastructure Engineer in Zürich, on-site. Start date ASAP. You will design the systems that underpin our robot learning, spanning data pipelines, tooling, and model deployment from day one.

You will build a tiered data processing platform, operate training infra with containerized deployments and cloud GPUs, and ship models to production with cloud and edge inference, ensuring safe deployments and robust evaluation.

Qualifikationen

• 5+ years of professional SWE experience with data platforms or ML infrastructure.
• Strong Python knowledge and typed languages (Rust, Go, C++, ...).
• Experience in data pipelines, storage, and schema evolution.
• Cloud training: GPU provisioning, reproducible setups, containers, model registry.
• Hands-on ML experience: dataloader throughput and GPU utilization.
• SWE foundations: IaC, code reviews, architectural changes, internal tooling.

Aufgaben

• Build a tiered data processing platform from ingestion to versioned training dataset generation.
• Build and operate the training infrastructure by using containerized deployments and cloud GPU provisioning.
• Ship models to production with cloud and edge inference and build the evaluation harness to guarantee safe deployments.
• Create and maintain internal data quality and inspection tooling.

Kenntnisse

Python

Rust

Go

C++

Data pipelines

Cloud GPUs

Tools

Docker

Terraform

Kubernetes

Jobbeschreibung

We are hiring a AI Infrastructure Engineer

Start date: ASAP

Zürich, Switzerland (on-site, remote not possible)

Full-time (100%)

Your role

As an AI infrastructure SWE you will build the systems that underpin our robot learning. You will work across data pipelines, internal tooling, and model deployment from day one as we build the foundations of our ML infrastructure.

What you’ll be doing:

• Build a tiered data processing platform from raw ingestion to versioned training dataset generation

• Build and operate the training infrastructure by using containerized deployments and cloud GPU provisioning

• Ship models to production with cloud and edge inference and build the evaluation harness to guarantee safe deployments

• Create and maintain internal data quality and inspection tooling

What you should have:

• 5+ years of experience in a professional SWE environment building production software with a significant focus on data platforms or ML infrastructure

• Strong Python knowledge and comfortable in a typed language (Rust, Go, C++, ...)

• Experience in data pipelines and storage: tiered architecture, workflow orchestration, backfills, and schema evolution

• Cloud training experience: you have provisioned GPU instances and trained in a reproducible setup, from containerized deployments to a model registry

• Hands-on ML experience: you have trained models and understand dataloader throughput, GPU utilization, and can debug slow or stalled training runs

• Strong SWE foundations: You work with IaC and code reviews, propose architectural changes and refactors, and build internal tooling and automation

These skills are a plus:

• Edge inference deployment (Jetson or similar) with TensorRT, ONNX, quantization

• Multimodal and time-series data: video pipelines, sensor logs, MCAP, time alignment across sources

• Distributed training and training performance optimization

• GPU cluster management and job orchestration

• Rust in production

Don't worry if you don't hit every check-mark. We value people who learn fast and care about building great products.