Senior ML Infrastructure Engineer

Prior Labs

BerlinHybridpay unclear

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Who we are Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched. Tables run every clinical trial, every financial model, every scientific experiment, every business decision, and no one had built a foundation model that truly understood them. Until now. What LLMs did for language, we're doing for tables. The next modality shift in AI is happening, and we're hiring the team that makes it. Momentum. We pioneered tabular foundation models and are now the world-leading organization in structured-data ML. Our TabPFN v2 model was published as a Nature cover story and set a new state of the art for tabular machine learning. Since release we've scaled model capabilities 20x+, passed 3.5M+ downloads and 7,500+ GitHub stars, and are seeing accelerating adoption across research and industry - from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi to improving clinical-trial decisions with BostonGene . The hardest work is ahead. We're scaling tabular foundation models to millions of rows, thousands of features, real-time inference, and entirely new data modalities, while building the infrastructure to run them in production across some of the most demanding industries on earth. These are open problems no one else is working on at this level. Our team. We're a small, highly selective team of 30+ engineers, researchers, and GTM specialists, with backgrounds spanning Google, Apple, Amazon, DeepMind, Meta, Microsoft Research, G-Research, Jane Street, Goldman Sachs, and CERN. We're led by Frank Hutter , Noah Hollmann , and Sauraj Gambhir , and advised by world-leading AI researchers including Bernhard Schölkopf and Turing Award winner Yann LeCun. We ship fast, do top-tier research, and hold each other to an extremely high bar. What's next. In 2025 we raised €9m pre-seed led by Balderton Capital, backed by leaders from Hugging Face, DeepMind, and Black Forest Labs. The next phase of growth is here, which makes this an ideal time to join. About the Role We spend tens of millions per year on GPU compute to train tabular foundation models. That's not a target, it's what we're running today, and it's growing. The person who owns this infrastructure makes decisions worth millions of dollars: cluster architecture, scheduling efficiency, provider strategy, hardware selection. A wrong call costs six figures. Today we run Slurm on GCP across multiple clusters. We're scaling to multi-cluster, multi-provider infrastructure and evaluating new hardware generations as they come online. You own the full stack, from cluster operations and cost optimization to distributed training performance and the tooling layer that keeps researchers moving fast. You work directly with the research team and understand what they're doing well enough to make infrastructure decisions that actually help them. And this isn't a pure support role. We operate an open environment. If you've got the next SOTA tabular architecture up your sleeve, go ahead and train it. What you'll work on: Own and evolve multi-cluster GPU infrastructure. Slurm on GCP today, multi-provider and new hardware tomorrow. Architecture, scheduling, reliability, cost optimization Drive GPU utilization and training throughput: profiling, memory optimization, communication bottlenecks, systems-level debugging of distributed training across large runs Architect the next generation of our infrastructure: multi-cluster orchestration, new GPU generations, provider diversification, capacity planning against growing compute demands Build the developer productivity layer: CI pipelines, experiment tracking, model registry, data processing, and internal tooling that keeps research iteration speed high Own the compute budget. You understand cost per FLOP across providers and hardware, and you hate wasted compute Tech stack: Slurm, GCP, Docker, wandb, GitHub Actions, uv, PyTorch, Triton You may be a good fit if you have: 5+ years building and operating production GPU infrastructure or distributed training systems at scale. At a major AI lab, a well-funded ML startup, or an HPC environment Deep hands-on experience with Slurm and cluster management. You've debugged scheduling failures, optimized utilization across multi-tenant GPU workloads, and operated infrastructure where downtime has real cost Expert-level systems thinking: memory bandwidth, GPU profiling. You reason about hardware, not configs Strong Python and genuine fluency with PyTorch internals. Enough to profile a training run and tell whether the bottleneck is data loading, communication, or compute Track record of making infrastructure decisions that measurably improved training throughput or cost efficiency Strong AI tooling skills. You use Claude Code, Cursor, or similar fluently to move fast without sacrificing quality Bonus: Experience operating at tens-of-millions-scale GPU spend Multi-cloud or hybrid HPC/cloud infrastructure experience Triton, CUDA, or custom kernel experience Experience scaling from single cluster to multi-cluster orchestration Background building experiment tracking, model registry, or ML pipeline tooling Life at Prior Labs We're a small, ambitious team solving one of the hardest problems in AI, and we're just getting started. You'll work closely with world-class researchers and builders who care deeply about the quality of their craft, the impact of their work, and the people they work with. We move fast, we think rigorously, and we take the time to do things right. If you're excited by hard problems, motivated by real-world impact, and want to be part of building something that matters, we'd love to hear from you. We're building our teams in Berlin, Freiburg, and New York and we believe that when you're working on something as hard and exciting as TabPFN, being in the same room matters. Most of our roles are based in one of our offices but great people come from everywhere, and in exceptional cases we're open to remote. This usually involves frequent travel to one of our offices and the whole company comes together regularly for offsites to think, build, and celebrate together. Our Commitments We believe the best products and teams come from a wide range of perspectives, experiences, and backgrounds. That's why we welcome applications from people of all identities and walks of life, especially anyone who's ever felt discouraged by "not checking every box." We're committed to creating a safe, inclusive environment and providing equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are. We care about how your data is handled. Read our Recruiting Privacy Notice to see exactly what we collect, why, and how long we keep it. Find more English Speaking Jobs in Germany on Arbeitnow Tags: Engineering & Science

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