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myitjob GmbH in Zurich, hybrid, is seeking an experienced ML infrastructure engineer to build and operate scalable AI platforms powering A1's AI products. You will design systems for training, evaluation, deployment, inference, and experimentation, while building production-grade pipelines and tooling.
Join a team focused on delivering reliable, high-performance ML infrastructure. The role involves collaboration with AI researchers and engineers to optimize reliability, latency, and cost, in a
Zurich, hybridWorkload: Full-time
Build and operate the ML infrastructure and platforms powering A1's AI products
Design systems for model training, evaluation, deployment, inference, and experimentation
Build and optimise model serving and inference infrastructure for high-throughput and low-latency workloads
Improve reliability, scalability, latency, and cost efficiency of AI systems
Develop reliable pipelines for data preparation, training, evaluation, model release, and continuous improvement
Build platforms and tooling that enable AI engineers and researchers to experiment, evaluate, and ship models faster
Develop evaluation and benchmarking infrastructure to measure model quality, performance, and regressions
Build production observability, monitoring, tracing, and alerting for AI/ML workloads
Improve AI systems across reliability, scalability, latency, throughput, and cost
Identify bottlenecks across the ML stack and continuously improve system performance
Work closely with AI engineers, researchers, and product teams to turn evolving model requirements into production-ready infrastructure
Strong software engineering fundamentals and experience building production systems
Experience building ML infrastructure, platforms, or production machine learning systems
Experience with model deployment, inference, evaluation, or data pipelines
Strong understanding of distributed systems and system reliability
Ability to write clean, maintainable, production-quality code
Comfortable working in ambiguous, fast-moving environments
Bias toward ownership, experimentation, and continuous improvement
Python
PyTorch / JAX
LLM and ML serving infrastructure such as vLLM, SGLang, or TensorRT-LLM
Cloud infrastructure
Distributed systems
ML/data pipelines and workflow orchestration
GPU infrastructure and performance tooling
Vector databases and retrieval infrastructure
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