Application Guide
How to Apply for Senior Software Engineer, GPU Cluster Infrastructure
at FAR AI
🏢 About FAR AI
FAR AI is a nonprofit research incubator focused on making AI systems trustworthy and beneficial to society, tackling projects too resource-intensive for academia but not yet commercially viable. Working here means contributing directly to safety-focused AI research infrastructure that supports cutting-edge experiments, rather than optimizing ad-tech or consumer products. The remote-first, mission-driven environment attracts engineers who want their GPU cluster expertise to serve the public good.
About This Role
As a Senior Software Engineer on GPU Cluster Infrastructure, you'll own the Kubernetes-based compute backbone that powers FAR AI's research, from node lifecycle and driver rollouts to fair-share batch scheduling and multi-tenant quotas. You'll also design distributed storage for massive datasets and checkpoints, and harden platform security through access controls and network policies. This role is impactful because reliable, secure, and efficient GPU infrastructure directly determines how quickly and safely AI safety research can progress.
💡 A Day in the Life
You might start by checking cluster health and reviewing any overnight alerts, then spend time tuning scheduler quotas for a new research team or testing a driver rollout in a staging namespace. Later, you could collaborate with researchers on storage performance for a large checkpointing job, or implement network policies to isolate a sensitive experiment. The work is a mix of hands-on operations, design, and cross-team collaboration, all in service of accelerating safe AI research.
🚀 Application Tools
🎯 Who FAR AI Is Looking For
- Deep hands-on experience operating large-scale Kubernetes clusters, especially with GPU workloads, node lifecycle management, and driver/CUDA rollouts.
- Proven ability to design and implement multi-tenant batch scheduling systems with queues, quotas, priorities, and fair-share allocation (e.g., Slurm, Volcano, Kueue, or custom schedulers).
- Strong background in distributed storage systems for high-performance AI workloads, including fault tolerance, checkpointing, and dataset access patterns.
- Security-minded engineer who can implement access controls, network policies, and workload isolation in a multi-tenant research environment.
- Comfortable working remotely and asynchronously, with a collaborative mindset suited to a nonprofit research incubator.
📝 Tips for Applying to FAR AI
Highlight specific Kubernetes GPU operator experience (e.g., NVIDIA GPU Operator, device plugins, MIG) and quantify cluster scale you've managed.
Describe any multi-tenant scheduling systems you've built or operated, naming the technologies (e.g., Volcano, Kueue, Slurm) and how you handled quotas and fair-share.
Emphasize distributed storage projects for AI/ML, including how you balanced performance, fault tolerance, and cost for datasets and checkpoints.
Show familiarity with security hardening in Kubernetes—network policies, RBAC, PodSecurity, and workload isolation—and tie it to protecting research integrity.
Connect your work to FAR AI's mission by explaining why you want to support AI safety research infrastructure rather than commercial cloud or ad-tech.
✉️ What to Emphasize in Your Cover Letter
['Your direct experience operating GPU-accelerated Kubernetes clusters at scale, including node lifecycle and driver rollouts.', 'A concrete example of designing or managing batch scheduling with multi-tenancy, quotas, and fair-share for research or ML workloads.', 'Your approach to building distributed storage for datasets and checkpoints that is both performant and fault-tolerant.', "Why FAR AI's nonprofit, safety-focused mission resonates with you and how you'd bring a security-first mindset to their research platform."]
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read FAR AI's website and recent publications to understand their research agendas and how infrastructure enables them.
- → Look into FAR AI's technical blog or public repos for any details on their Kubernetes, scheduling, or storage stack.
- → Research common GPU cluster orchestration tools (e.g., NVIDIA GPU Operator, Volcano, Kueue, Kubeflow) and be ready to discuss their trade-offs.
- → Understand the unique constraints of nonprofit AI research infrastructure: limited budgets, grant-funded projects, and the need for reproducibility and security.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Focusing only on generic DevOps or cloud experience without highlighting GPU-specific Kubernetes work.
- Ignoring the multi-tenancy and fair-share aspects of the role, which are central to supporting multiple research teams.
- Treating the position as a standard commercial infrastructure job and failing to connect your motivation to FAR AI's safety and nonprofit 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:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!