Sensity is building a new foundational security layer for the internet: an all-inclusive solution for detecting fake identities, documents, images, video, and audio, whether AI-generated or digitally altered. We secure enterprise clients on four continents in finance, marketplaces, insurance, and cybersecurity and threat intelligence, and we support the work of dozens of government agencies.

Founded in 2018 by AI researchers, Sensity is the world's first company specialised in the detection of AI-generated content. We are backed by Betaworks (HuggingFace, Giphy), Amadeus Capital (Graphcore, Improbable), the European Commission's Horizon-KTE, Visa Everywhere, and Google News Initiative. Sensity has been featured by the likes of the New York Times, the Financial Times, and Bloomberg.

This is a hybrid role based in the Netherlands. You must be within reasonable travelling distance of our data center outside Amsterdam, since this role requires regular and occasional emergency on-site presence.

THE ROLE

We're hiring an Infrastructure Engineer to own the physical and operational backbone of Sensity's compute: our self-managed data center, the servers and GPUs that run our training workloads, and the operating environment our engineering team relies on every day.

This is hands-on. Some weeks you'll rack new hardware and swap drives. Others you'll be in a terminal upgrading kernels and helping the ML team find the bottleneck in a training run.

WHAT YOU'LL WORK ON

• Own the hardware at our self-managed data center: install and connect servers, GPUs, storage, and network gear; keep the inventory accurate.
• Run the operating environment: Linux installs and upgrades, drivers, firmware, kernels, and security patching.
• Manage storage and data: RAID setup and recovery, filesystems, NFS, dataset and model artefact moves, backups, and cleanups.
• Respond to incidents, including to the data center when needed.
• Support engineering and ML as their infrastructure partner: run diagnostics, speed tests, and GPU benchmarks to find real bottlenecks.
• Plan and execute capacity expansions: spec new servers and GPU nodes, deploy them, bring them into production.

OUR ENVIRONMENT

• Compute. Self-managed bare-metal servers with NVIDIA GPUs, used for ML training.
• OS. Linux (Ubuntu primarily), some Windows for specific workloads.
• Storage. RAID arrays, large dataset volumes; mix of NVMe and spinning disks.
• Networking. On-prem networking gear, VPNs, and connectivity to our cloud footprint.
• ML stack we support. PyTorch, CUDA, common training and inference tooling.

WHAT YOU'LL BRING

• 3+ years owning production servers, ideally including time inside a real data center, not just cloud.
• Strong Linux sysadmin skills: kernel, networking, storage, package management, systemd, troubleshooting under pressure.
• Networking fundamentals: TCP/IP, VLANs, routing, firewalls, VPNs.
• Working knowledge of RAID in practice, including degraded arrays and recovery.
• Comfortable scripting in Bash and Python.
• Willing and able to be physically present at the data center on short notice.

THE KIND OF PERSON WE'RE LOOKING FOR

• Hands-on. Equally comfortable in a terminal and in a server room.
• Methodical under pressure. When something is on fire, you isolate rather than guess.
• Collaborative. You can talk to ML researchers, backend engineers, and vendors, and translate between them.

BONUS

• Working understanding of GPU compute: drivers, CUDA.

WHAT WE OFFER

• A founder-led team of AI researchers and engineers working at the forefront of AI research.
• Access to modern AI development tooling.
• International and fast growing company.

We are committed to equal employment opportunity regardless of culture, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity or any other basis as protected by applicable law.