Mirendil
Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We believe accelerating scientific discovery is one of the most powerful ways to improve the future of humanity, and that AI will play a central role in making that possible.
We are building a frontier AI research company and training our own models end-to-end. Our work spans areas such as model training, reinforcement learning, reasoning systems, and infrastructure for large-scale experiments. Our team includes researchers and engineers from Anthropic, Google DeepMind, xAI, OpenAI, Microsoft, Apple, and MIT.
The Role
We are looking for an Infrastructure Engineer to own the compute and cloud foundation that frontier AI research runs on. The systems you build determine how fast we can train models, how reliably experiments run, and how efficiently we scale. Some example areas you might work on:
- Sandboxing and secure execution - design isolated environments where agents and untrusted code can run, use tools, and reach external services
- Kubernetes and multi-cluster compute - operate CPU and GPU clusters as one platform with scheduling, autoscaling, and multi-tenant isolation
- Training and inference infrastructure - understand the resource and scheduling demands of research, training, and inference workloads and build the platform capabilities those workloads need
- Infrastructure for long-running agents - build the state management system to handle checkpointing, recovery, and resumption across failures
- Networking - build the networking layer across clouds, clusters, and hosts: routing, peering, load balancing, and network isolation
If you're excited about building the infrastructure backbone of a frontier AI research lab - where your systems directly determine research velocity - we'd love to hear from you.
We offer a base salary of $350,000–$500,000 and a meaningful equity grant, depending on experience and background, along with competitive benefits.