FAR.AI is a non-profit AI research institute working to ensure advanced AI is safe and beneficial for everyone. Our mission is to facilitate breakthrough AI safety research, advance global understanding of AI risks and solutions, and foster a coordinated global response.
🏢 About FAR.AI
We’re structured to support that work from early research through real-world adoption:
Independent by design. We can pursue what's most impactful based on our theory of change and share what we find publicly.
A portfolio approach. Rather than focus on one single direction, we run diverse bets across the safety stack. We take promising ideas from initial experiments to deployment, informed by red-team partnerships with frontier labs and governments.
Serious infrastructure for ambitious research. A dedicated engineering team runs our compute cluster and experiment-scaling stack, so researchers spend their time on research instead of on infra.
Setting the standard. Our events convene key decision makers; our red-team works with frontier developers and governments; and our communications inform the public. Together, this drives adoption and sets the new standard in safety.
Since our founding in July 2022, we've grown to 50+ staff, published 40+ academic papers, and convened leading AI safety events. Our work is recognized globally, with publications at premier venues such as NeurIPS, ICML including a Best Paper Honorable Mention in 2026, and ICLR, and features in the Financial Times, Nature News, Wired Magazine and MIT Technology Review. We conduct pre-deployment testing on behalf of frontier developers such as OpenAI and independent evaluations for governments including the EU AI Office and publish the AI Security Leaderboard based on our red-teaming expertise. We help steer and grow the AI safety field through developing research roadmaps with renowned researchers such as Yoshua Bengio; running FAR.Labs, an AI safety-focused co-working space in Berkeley housing 40+ members; and supporting the community through targeted grants to technical researchers.
🎯 The Role
You'd be the infrastructure sub-team's lead and one of its engineers, with at least half your time on technical work. You'd own the platform's technical direction and roadmap, deciding what we build and how we run it in collaboration with the research teams and the rest of Foundations, and you'd hire and grow the team.
In frontier AI research, working out the infrastructure is often part of the science. You'd work directly with researchers and other engineers to keep our large-scale experiments performant and fault-tolerant.
✅ Key Responsibilities
Set the platform's technical direction and own its roadmap. You decide which systems we run, how we schedule and store across providers, and what we measure, from utilization and queue wait to failure rates.
Stay in the work. You own architecture and the scheduling and storage design, and you debug the failures that cross layers, such as node health, GPU and fabric faults, and multi-node job hangs.
Hire and grow a small team of senior engineers. You set priorities and ownership, scope projects, and give regular feedback and coaching.
Set security direction for a shared cluster where AI agents run experiments, covering identity and access, workload isolation, and sandboxing.
Decide how we operate, from the on-call rotation and incident response to postmortems, fault tolerance, and observability, and take part in it alongside the team.
Be the escalation point for research teams and for providers, and turn recurring problems into platform fixes.
📌 Required Qualifications
You've led engineers as a manager, tech lead, or project lead, setting technical direction, scoping work, and giving feedback. You want to manage people directly; prior direct reports aren't required.
You have 5+ years in systems or infrastructure engineering on production Linux, running GPU, HPC, or large-scale batch platforms, and you've owned systems from design through operation. You have depth in at least one of scheduling, storage, networking, security, or GPU systems, and enough breadth to review designs in the rest.
You've run production Kubernetes for GPU workloads.
⭐ Desirable Experience
Experience with multi-cloud or multi-provider infrastructure.
Experience with large-scale distributed systems or HPC environments.
Experience with container orchestration and scheduling systems.
Experience with infrastructure as code tools (e.g., Terraform, Pulumi).
Experience with monitoring and observability tools (e.g., Prometheus, Grafana).
Experience with security best practices for cloud infrastructure.
🎁 Benefits
Competitive salary and benefits package.
🛂 Visa & Eligibility
Eligibility to work remotely from any location is required.
