Engineering Manager

il y a 1 jour


Marseille, France Hivenet Temps plein

Overview We're building the future of cloud infrastructure. Hivenet is a distributed cloud platform offering GPU computing for AI/ML workloads, secure file storage, and fast file transfer—all powered by a sustainable, decentralized architecture that reduces carbon footprint by 77% compared to traditional data centers. We're a well-funded, remote-first team scaling fast across EMEA. About Hivenet We're building the future of cloud infrastructure. Hivenet is a distributed cloud platform offering GPU computing for AI/ML workloads, secure file storage, and fast file transfer—all powered by a sustainable, decentralized architecture that reduces carbon footprint by 77% compared to traditional data centers. We're a well-funded, remote-first team scaling fast across EMEA. About the Team The Compute GPU team owns our core GPU cloud infrastructure: Platform Services, billing systems, team management, and the foundational components that power Hivenet Compute. We serve RTX 4090/5090 instances across multiple regions to AI researchers and enterprises building production AI systems. What You'll Do Lead and grow a team of 4-6 engineers while staying hands-on with architecture and code Own the technical roadmap for GPU infrastructure, Platform Services, and billing systems Drive engineering excellence: code reviews, system design, incident response, and team rituals Partner with Product to shape the Compute roadmap and deliver against quarterly objectives Build scalable systems using Kubernetes, Golang, and our GitOps/FluxCD deployment pipeline What You Bring 5+ years of software engineering experience, including 2+ years leading engineering teams Strong hands-on experience with Kubernetes, containerization, and cloud infrastructure Proficiency in Golang or similar systems languages Track record of shipping production systems at scale Experience with the EM/PM partnership model and roadmap delivery Proficiency with AI-assisted development tools and agentic coding workflows Demonstrated ability to use AI coding assistants effectively—rapidly prototyping, debugging, and refactoring while maintaining strong engineering judgment and production standards Bonus Points Experience with GPU infrastructure, CUDA/ROCm, or ML/AI workloads Familiarity with KubeVirt, KVM, or virtualization technologies Background in billing systems or usage-based pricing models The Reality This is an early-stage startup—you'll wear many hats We move fast and expect ownership mentality You'll be building processes, not following existing ones You'll be player-coach: ~40% hands-on technical, ~60% leadership and strategy The team is small but growing—you'll shape engineering culture from the ground up What We Offer Full remote work from anywhere (EMEA timezone preferred) Equipment budget to set up your ideal workspace Company offsites to connect with the team Career growth in a scaling engineering organization Work on cutting-edge distributed systems and AI infrastructure #J-18808-Ljbffr


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