Senior AI Infrastructure Engineer (Zürich, 100%)
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Das ist der Job
We value people who learn fast and care about building great products.
Darum lohnt es sich
ph3Senior AI Infrastructure Engineer (Zürich, 100%) /h3 pStart date: ASAP /p pZürich, Switzerland (on-site, remote not possible) /p pFull-time (100%) /p h3We are hiring an AI Infrastructure Engineer /h3 pAs 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. /p h3Your role /h3 pAs 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. /p h3What you’ll be doing: /h3 ul liBuild a tiered data processing platform from raw ingestion to versioned training dataset generation /li liBuild and operate the training infrastructure by using containerized deployments and cloud GPU provisioning /li liShip models to production with cloud and edge inference and build the evaluation harness to guarantee safe deployments /li liCreate and maintain internal data quality and inspection tooling /li /ul h3What you should have: /h3 ul li5+ years of experience in a professional SWE environment building production software with a significant focus on data platforms or ML infrastructure /li liStrong Python knowledge and comfortable in a typed language (Rust, Go, C++, …) /li liExperience in data pipelines and storage: tiered architecture, workflow orchestration, backfills, and schema evolution /li liCloud training experience: you have provisioned GPU instances and trained in a reproducible setup, from containerized deployments to a model registry /li liHands‑on ML experience: you have trained models and understand dataloader throughput, GPU utilization, and can debug slow or stalled training runs /li liStrong SWE foundations: You work with IaC and code reviews, propose architectural changes and refactors, and build internal tooling and automation /li /ul h3These skills are a plus: /h3 ul liEdge inference deployment (Jetson or similar) with TensorRT, ONNX, quantization /li liMultimodal and time‑series data: video pipelines, sensor logs, MCAP, time alignment across sources /li liDistributed training and training performance optimization /li liGPU cluster management and job orchestration /li liRust in production /li /ul h3Interested? /h3 pShoot us a message to , including (AII) in the email subject. /p pDon’t worry if you don’t hit every check‑mark.
Just give it a go and apply. /p /p #J-18808-Ljbffr
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