AI Engineer

At VEED, we're a generative AI company building the AI video creation platform made for social. It's where millions of marketers imagine and generate branded videos, in minutes. We're powered by frontier models and VEED Fabric, our own image-to-video model. Our APIs also run AI video inside leading creative products. Our team is the engine behind VEED's state-of-the-art models. We own the GPU infrastructure that powers everything from real-time video generation to our API platform, and we're scaling fast. We work at the intersection of cutting-edge ML research and production engineering. If it runs on a GPU, we're behind it. Our Stack: Python (primary), TypeScript and Go (cross-team collaboration). GPU infrastructure across GCP cloud provider and neo-cloud providers. PyTorch model ecosystem for deployment and serving.

Responsibilities

Deploy and operate our AI models across GPU infrastructure, ensuring the reliability and performance our enterprise customers depend on; Own monitoring, alerting, and incident response, keeping our systems healthy and our SLAs intact; Manage failovers across multiple cloud and neo-cloud GPU providers, balancing cost, performance, and risk; Build data pipelines that support model training and help us iterate faster on new model releases; Work as a technical peer: contribute ideas, challenge assumptions, and help make better decisions on architecture and tooling; Influence the design of our API platform by suggesting technologies and approaches as we build the infrastructure layer that enterprise customers will rely on; Source creative solutions to GPU availability and cost constraints, working with both major cloud providers and specialist neo-cloud partners

Requirements

Hands-on with GPUs (run models, provisioned and managed GPU infrastructure yourself, keep systems running reliably at scale); Strong in Python (it's your go-to language); Experienced in DevOps or MLOps (operational rigour across monitoring, alerting, incident response and failover in production); Familiar with neo-cloud providers (understand trade-offs of specialist GPU providers beyond the big three, cost benefits, risks, and manage both); A strong technical partner (comfortable working closely with another engineer, pushing back when disagreeing, contributing to technical decisions); Hands-on with the ML stack (deployed PyTorch models to production and handle common failure modes); Excited by the problem of GPU sourcing (cost, performance, and availability constraints require creative thinking)

Nice to have

TypeScript; Go

Benefits

Monthly subsidy programme; Unlimited paid holidays; Home office set-up (IT Equipment program with laptop, monitors, headsets/earbuds, keyboards and more); Mental health benefit (partnered with Spill for confidential mental health support)