In ~1 minute you get: who interviews you, the likely questions answered from your CV, and your CV tailored to this job. Your first dossier is free.

About ActAI There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations. Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things. About the Role As an ML Platform Engineer, you will build the infrastructure and systems that power ActAI's AI capabilities. You will design and operate the systems behind the AI stack, from model training and evaluation to deployment, inference, observability, and continuous improvement. You will work closely with AI engineers, researchers, and product engineers to turn models into reliable, scalable, and cost-efficient production systems. You will build the platforms, tooling, and infrastructure that enable the team to experiment quickly and bring AI capabilities to production with confidence. Tech Stack 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 Ideal Experience 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 Outcomes AI infrastructure reliably supports production workloads at scale Models can be trained, evaluated, deployed, and improved efficiently Inference systems deliver strong latency, throughput, reliability, and cost efficiency ML pipelines are reproducible, observable, maintainable, and robust Model and infrastructure regressions are detected quickly and diagnosed efficiently Common ML infrastructure capabilities become reusable platform primitives rather than being rebuilt for every AI product The AI stack can evolve rapidly as new models, architectures, and inference techniques emerge Find Jobs in Germany on Arbeitnow

Machine Learning Platform Engineer

Don't apply unprepared

Machine Learning Platform Engineer

Don't apply unprepared