Product Engineer
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We are looking for a Product Engineer to own and ship the products that put this capability in the hands of engineers, data scientists, and researchers.
Ramat Gan · Full-time · Hybrid
DevOps Engineer
We are looking for a DevOps Engineer to own and scale the infrastructure behind our AI systems.
Infrastructure · Ramat Gan · Full-time · Hybrid
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01
About the role
We are looking for a DevOps Engineer to own and scale the infrastructure behind our AI systems. This role combines hands-on management of our large-scale GPU cluster with classical DevOps responsibilities across cloud infrastructure, CI/CD, Kubernetes, monitoring, security, and developer productivity.
doubleAI is building next-generation reasoning and code-optimization agents that push the frontier of artificial expert intelligence. Our models are built on world-class infrastructure: large-scale GPU clusters, fast networking, reliable scheduling, deep observability, secure cloud environments, and CI/CD pipelines that allow our team to move quickly without compromising reliability.
We are looking for someone deeply technical, hands-on, and AI-native in mindset — someone who understands that compute, automation, reliability, and security are foundational to building an AI company.
02
What you'll do
Own cluster reliability, availability, monitoring, alerting, capacity planning, and incident response.
Configure, maintain, and troubleshoot GPU servers, networking equipment, switches, InfiniBand fabrics, storage, and related infrastructure.
Deploy, maintain, and improve infrastructure around Slurm, Kubernetes, and GPU workload orchestration.
Manage and improve our cloud environment, including compute, networking, storage, IAM, security groups, cost visibility, and production infrastructure.
Build, maintain, and improve CI/CD pipelines.
Work closely with engineers to understand infrastructure bottlenecks and improve development, training, inference, and deployment velocity.
Contribute to security best practices across cloud, network, CI/CD, access control, secrets management, and production systems.
03
What we're looking for
- Strong hands-on experience managing Linux-based infrastructure in production environments.
- Experience with classical DevOps responsibilities: CI/CD, cloud infrastructure, monitoring, automation, deployment systems, and production operations.
- Familiarity with Kubernetes in real-world production environments.
- Experience operating GPU clusters, HPC environments, AI infrastructure, or other high-performance compute systems.
- Familiarity with Slurm or similar workload schedulers.
- Practical knowledge of networking, including switch configuration, routing, VLANs, firewalls, high-throughput networking, and debugging network issues.
- Experience with InfiniBand, RDMA, high-performance interconnects, or similar low-latency networking technologies.
- Ability to troubleshoot across the full stack: hardware, firmware, BIOS, Linux kernel, drivers, CUDA, containers, networking, storage, cloud infrastructure, CI/CD, and schedulers.
- Strong scripting and automation skills using Bash, Python, Ansible, Terraform, or similar tools.
- A strong ownership mindset: you notice problems, fix root causes, automate repetitive work, and leave systems more reliable than you found them.
Nice to have
- Experience managing NVIDIA GPU clusters at scale.
- Familiarity with CUDA, NVIDIA drivers, NCCL, DCGM, MIG, GPUDirect RDMA, or GPU performance monitoring.
- Experience with Mellanox/NVIDIA networking equipment, InfiniBand fabric management, subnet managers, or RDMA debugging.
- Experience supporting large-scale ML training workloads using PyTorch, Megatron, TorchTitan, DeepSpeed, or similar frameworks.
- Experience in a startup or fast-moving research environment where priorities shift quickly and infrastructure must keep up.
Interested in this role?
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