Application Guide

How to Apply for Senior Software Engineer, GPU Cluster Infrastructure

at FAR.AI

🏢 About FAR.AI

FAR.AI is a nonprofit AI safety research organization focused on reducing risks from advanced AI systems, which means your infrastructure work directly supports safety-critical research rather than commercial products. Working here offers the rare combination of large-scale GPU systems challenges and a mission-driven, research-first culture.

About This Role

As a Senior Software Engineer on GPU Cluster Infrastructure, you'll own the Kubernetes-based GPU fleet that powers AI safety experiments—from node lifecycle and scheduling to distributed storage and security hardening. Your work enables researchers to run multi-node training reliably, safely, and efficiently at scale, making you a force multiplier for the entire research agenda.

💡 A Day in the Life

A typical day might involve triaging cluster health, planning or executing a staged Kubernetes upgrade, tuning scheduling quotas for research teams, and debugging a multi-node training job or NCCL issue. You'll also collaborate with researchers on storage and checkpointing needs, and implement security hardening for shared GPU workloads.

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🎯 Who FAR.AI Is Looking For

  • 3+ years running production Linux systems or infrastructure engineering on GPU, HPC, or batch platforms, with deep Kubernetes operational experience (node lifecycle, upgrades, capacity planning).
  • Hands-on with batch scheduling, multi-tenancy, quotas, priorities, and fair-share allocation across competing research teams.
  • Experienced designing and managing distributed storage for datasets, checkpoints, and backups, plus debugging multi-node training and NCCL issues.
  • Security-minded engineer who can harden identity, access control, network policy, workload isolation, and sandboxing for autonomous agents.
  • Comfortable with infrastructure-as-code and staged rollouts with safe rollback in a remote, research-driven environment.

📝 Tips for Applying to FAR.AI

1

Frame your resume around GPU cluster operations: highlight specific Kubernetes fleet sizes, node counts, and upgrade/rollback procedures you've owned.

2

Quantify scheduling and multi-tenancy impact—e.g., how you implemented quotas, priorities, or fair-share allocation and what it improved for users.

3

Describe distributed storage and checkpointing work in detail, including fault tolerance for multi-node training and any NCCL debugging you've done.

4

Show security hardening experience explicitly: identity, RBAC, network policy, workload isolation, and sandboxing for agents or untrusted workloads.

5

Mention infrastructure-as-code tools (Terraform, Ansible, Helm, etc.) and staged rollout practices; FAR.AI needs someone who can operate safely at scale.

✉️ What to Emphasize in Your Cover Letter

['Your direct experience operating Kubernetes GPU clusters day-to-day, including node lifecycle, upgrades, capacity planning, and safe rollbacks.', 'Concrete examples of owning batch scheduling, multi-tenancy, quotas, priorities, and fair-share allocation across teams.', 'Your approach to distributed storage for datasets, checkpoints, and backups, and how you ensure multi-node training fault tolerance and debug NCCL issues.', "Why AI safety infrastructure matters to you and how you'd harden platform security for autonomous agents in a research nonprofit setting."]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read FAR.AI's published research and mission materials to understand the specific AI safety risks they address and how infrastructure enables that work.
  • → Look into their public engineering blog, talks, or GitHub to learn about their current GPU cluster scale, tooling, and infrastructure challenges.
  • → Research common GPU cluster and Kubernetes patterns used in AI safety and HPC environments, including scheduling, storage, and security best practices.
  • → Understand the nonprofit research context: how funding, collaboration, and open science may shape infrastructure priorities and constraints.
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💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through how you've operated a Kubernetes GPU cluster: node lifecycle, upgrades, capacity planning, and staged rollouts with rollback.
2 How have you implemented batch scheduling, multi-tenancy, quotas, priorities, and fair-share allocation across research or engineering teams?
3 Describe your design for distributed storage supporting datasets, checkpoints, and backups, including fault tolerance for multi-node training.
4 How do you debug NCCL and multi-node training issues in production? Give a specific example.
5 How would you harden platform security—identity, access control, network policy, workload isolation, and sandboxing—for autonomous agents on shared GPU infrastructure?
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⚠️ Common Mistakes to Avoid

  • Submitting a generic infrastructure resume that doesn't mention GPU clusters, Kubernetes operations, or multi-node training specifics.
  • Focusing only on commercial cloud or web-scale experience without addressing batch scheduling, quotas, fair-share, and research multi-tenancy needs.
  • Ignoring the security and sandboxing aspects of the role—especially workload isolation and autonomous agent safety—which are central to FAR.AI's mission.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to FAR.AI!

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