Application Guide

How to Apply for Tech Lead Manager, GPU Cluster Infrastructure

at FAR AI

🏢 About FAR AI

FAR AI is a nonprofit research incubator focused on making AI systems trustworthy and beneficial, tackling projects too resource-intensive for academia but not yet commercialized by industry. Working here means contributing to safety-oriented research at the frontier of AI, with the unique challenge of building infrastructure that supports both cutting-edge experiments and secure multi-agent environments.

About This Role

As Tech Lead Manager for GPU Cluster Infrastructure, you'll split your time between hands-on engineering and leading a sub-team, setting technical direction for GPU clusters while ensuring reliability, security, and performance. You'll own systems design across scheduling, storage, networking, and GPU infrastructure, and establish operational practices for shared clusters used by AI agents. This role is pivotal in enabling FAR AI's research agendas by providing robust, secure, and scalable compute foundations.

💡 A Day in the Life

Your day might start with a stand-up with the infrastructure sub-team, followed by hands-on work debugging a networking issue in the GPU cluster. Later, you'd review a design proposal for a new scheduling system, interview a senior engineer candidate, and meet with researchers to understand their compute needs. You might end the day documenting an incident response plan or analyzing observability metrics to improve fault tolerance.

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Interview Prep

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🎯 Who FAR AI Is Looking For

  • Proven experience leading infrastructure teams while remaining deeply technical, with a track record of hiring and mentoring senior engineers.
  • Expertise in designing and debugging large-scale GPU clusters, including scheduling (e.g., Slurm, Kubernetes), high-performance storage (e.g., Lustre, Ceph), and networking (e.g., InfiniBand, RoCE).
  • Strong background in security for shared compute environments, including identity management, access control, and workload isolation for AI workloads.
  • Hands-on experience with operational practices such as incident response, observability (e.g., Prometheus, Grafana), and fault tolerance in production systems.

📝 Tips for Applying to FAR AI

1

Highlight specific GPU cluster projects you've led, including scale (number of GPUs, nodes), technologies used, and outcomes (e.g., utilization improvements, cost savings).

2

Emphasize your balance of technical and managerial skills: quantify team growth, mentorship impact, and your personal contributions to system design.

3

Showcase security experience in shared cluster environments, particularly with AI agents or multi-tenant workloads, detailing identity, access, and isolation mechanisms you've implemented.

4

Demonstrate familiarity with FAR AI's research focus by referencing their projects or publications, and explain how your infrastructure work would accelerate their mission.

5

Include metrics on operational improvements (e.g., uptime, incident reduction) and fault tolerance strategies you've deployed.

✉️ What to Emphasize in Your Cover Letter

1. Your experience leading infrastructure teams while staying hands-on, with concrete examples of technical contributions. 2. Deep knowledge of GPU cluster design across scheduling, storage, networking, and GPU technologies. 3. Security expertise in shared clusters, especially for AI workloads, covering identity, access, and isolation. 4. Alignment with FAR AI's mission and how you'll enable their research through reliable, secure infrastructure.

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Explore FAR AI's website, focusing on their research agendas and incubated projects to understand the types of AI workloads you'd support.
  • → Read FAR AI's publications or blog posts on trustworthy AI to grasp their technical and ethical priorities.
  • → Research common GPU cluster management tools (e.g., Kubernetes, Slurm, Prometheus) and security frameworks (e.g., zero-trust, RBAC) used in AI research environments.
  • → Look into recent advancements in GPU cluster infrastructure, such as NVIDIA DGX, InfiniBand, and distributed storage solutions, to speak fluently about current technologies.
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💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Design a GPU cluster for a multi-tenant AI research environment, covering scheduling, storage, networking, and security.
2 How do you balance hands-on engineering with managerial responsibilities? Provide examples.
3 Describe your approach to securing shared clusters running AI agents, including identity and workload isolation.
4 Walk us through a complex debugging scenario in a GPU cluster and how you resolved it.
5 How would you establish operational practices (incident response, observability, fault tolerance) for a new cluster?
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⚠️ Common Mistakes to Avoid

  • Focusing only on managerial experience without demonstrating current technical depth in GPU cluster infrastructure.
  • Ignoring security aspects of shared clusters, which is a key requirement for this role.
  • Providing generic statements about leadership without concrete examples of hiring, mentoring, and technical direction.

📅 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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