Application Guide
How to Apply for Tech Lead Manager, GPU Cluster Infrastructure
at Far.Ai
🏢 About Far.Ai
FAR.AI is a non-profit AI research institute founded in 2022, dedicated to ensuring advanced AI is safe and beneficial. Unlike many AI labs, it combines independent, public-interest research with a portfolio approach—running diverse bets across the safety stack and partnering with frontier labs and governments. Its dedicated engineering team builds serious infrastructure so researchers can focus on high-impact work, making it a unique place for infrastructure leaders who want to directly enable AI safety breakthroughs.
About This Role
As Tech Lead Manager for GPU Cluster Infrastructure, you will own the design, scaling, and reliability of FAR.AI's compute cluster and experiment-scaling stack. You'll lead a team of engineers while staying hands-on, ensuring researchers can run large-scale safety experiments without infra bottlenecks. This role is critical to FAR.AI's mission: your work directly accelerates research that informs global AI safety standards and policy.
💡 A Day in the Life
A typical day might involve a morning stand-up with your engineering team to prioritize cluster upgrades and troubleshoot issues, followed by hands-on work designing a new scheduling policy or debugging a networking bottleneck. In the afternoon, you might meet with researchers to understand their compute needs for an upcoming experiment, then mentor an engineer on a complex deployment. You'll regularly balance strategic planning with urgent operational tasks to keep the cluster running smoothly.
🚀 Application Tools
🎯 Who Far.Ai Is Looking For
- Proven experience managing and scaling GPU clusters (e.g., Kubernetes, Slurm, or custom orchestration) for ML workloads, with deep knowledge of NVIDIA GPUs, CUDA, and high-performance networking (InfiniBand, RoCE).
- Strong technical leadership: able to mentor engineers, set technical direction, and collaborate with researchers to translate their needs into robust infrastructure.
- Hands-on expertise in cloud and on-prem infrastructure, job scheduling, distributed training frameworks (PyTorch, TensorFlow), and monitoring/observability tools (Prometheus, Grafana).
- Commitment to FAR.AI's mission and comfort working in a non-profit, remote-first environment with a fast-paced, research-driven culture.
📝 Tips for Applying to Far.Ai
Highlight specific GPU cluster projects you've led—include scale (number of GPUs, nodes), technologies used, and outcomes (e.g., reduced job wait times, improved utilization).
Emphasize your experience managing engineers while remaining technically hands-on; FAR.AI values player-coaches who can code and lead.
Demonstrate familiarity with AI safety research workflows—mention how your infrastructure work enabled faster experimentation or reproducibility in past roles.
Show enthusiasm for FAR.AI's non-profit mission; connect your infrastructure work to enabling safety research that informs policy and frontier lab practices.
Tailor your resume to include keywords from the job description: GPU cluster, experiment-scaling, Kubernetes, Slurm, distributed training, etc.
✉️ What to Emphasize in Your Cover Letter
In your cover letter, focus on: (1) a concrete example of a GPU cluster you built or scaled, including technical details and impact on research velocity; (2) your philosophy on leading infrastructure teams in a research environment—balancing reliability, flexibility, and researcher autonomy; (3) why FAR.AI's mission resonates with you and how you see infrastructure as a lever for AI safety; (4) your experience collaborating with researchers to understand their compute needs and translating them into scalable solutions.
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read FAR.AI's published papers and blog posts to understand their research directions and how infrastructure supports them (e.g., large-scale experiments, red-teaming).
- → Explore the backgrounds of FAR.AI's engineering team and leadership to understand their technical stack and culture.
- → Look into FAR.AI's partnerships with frontier labs and governments to grasp the real-world impact of their work and the scale of experiments involved.
- → Review FAR.AI's events and public communications to align your application with their mission and current priorities.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Focusing only on generic infrastructure experience without tying it to ML/AI workloads—FAR.AI needs someone who understands the unique demands of GPU-accelerated research.
- Overemphasizing management while neglecting hands-on technical skills; this role requires both, and candidates who can't demonstrate recent coding or system design may be overlooked.
- Ignoring the non-profit, mission-driven context—candidates who seem motivated only by compensation or big-tech perks may not fit FAR.AI's culture.
📅 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!