Job description

fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products. As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on. ABOUT THIS ROLE: You are an experienced software engineer who is passionate about building large-scale computing platforms. You have experience building systems that remain reliable under high traffic, partial failures, and changing capacity. You know how to deliver reliability, performance, and scale with minimum operational load. WHAT YOU'LL DO: - Take ownership of one or more of the Rust/Python systems that power our AI inference and large-scale GPU computing platform - Design for 100x growth in workloads and GPU capacity, with reliable, low-latency execution across the globe - Use AI aggressively to accelerate development and automate repetitive operations, alerting, and recovery You will specialize in one or more of these critical areas: - Request routing and queuing on a global scale - App deployment and autoscaling - Worker orchestration and placement - GPU fleet autoscaling and capacity management - GPU fleet lifecycle automation: provisioning, diagnostics & recovery, upgrades, tracking - Global file storage, caching and distribution - Global IP backbone: private networking across datacenters, elastic public IPs, ACLs/firewalls, private connectivity - Workload execution: containerization, globally distributed filesystems, RAM/VRAM snapshotting QUALIFICATIONS/NICE TO HAVE: - 3+ years building and operating large-scale production systems, with a track record of reliability and scale - Strong Rust and/or Python skills - Strong systems fundamentals, with technical depth relevant to your specialization: distributed coordination, scheduling, fault tolerance, Linux, networking, storage, or capacity management - Experience building and using observability to drive performance and reliability decisions - Clear communication, sound technical judgment, and the initiative to move quickly, drive decisions across teams, and own systems from design through production - Multi-tenant compute platforms, AI inference or training infrastructure, GPU workload scheduling - High-performance systems programming: async runtimes, zero-copy, memory-safe concurrency - Linux systems engineering and fleet automation: configuration management, kernel and driver debugging, safe upgrades, and automated recovery - Distributed filesystems like JuiceFS and Lustre - Experience with Nvidia/AMD GPU infrastructure; DCGM, NVLink, RDMA, InfiniBand/RoCEv2 - Global networking and routing: BGP, ECMP, tunnels, eBPF, Geneve WHAT WE OFFER AT FAL: - Interesting and challenging work - A lot of learning and growth opportunities - Regular team events and offsites U.S. EQUAL EMPLOYMENT OPPORTUNITY INFORMATION: fal provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other classification protected by applicable law.

Requirements

Must-haves

As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.
Take ownership of one or more of the Rust/Python systems that power our AI inference and large-scale GPU computing platform
Design for 100x growth in workloads and GPU capacity, with reliable, low-latency execution across the globe
Use AI aggressively to accelerate development and automate repetitive operations, alerting, and recovery
You will specialize in one or more of these critical areas:
Request routing and queuing on a global scale
App deployment and autoscaling
Worker orchestration and placement
GPU fleet autoscaling and capacity management
GPU fleet lifecycle automation: provisioning, diagnostics & recovery, upgrades, tracking
Global file storage, caching and distribution
Global IP backbone: private networking across datacenters, elastic public IPs, ACLs/firewalls, private connectivity
Workload execution: containerization, globally distributed filesystems, RAM/VRAM snapshotting
3+ years building and operating large-scale production systems, with a track record of reliability and scale
Strong Rust and/or Python skills
Strong systems fundamentals, with technical depth relevant to your specialization: distributed coordination, scheduling, fault tolerance, Linux, networking, storage, or capacity management
Experience building and using observability to drive performance and reliability decisions
Clear communication, sound technical judgment, and the initiative to move quickly, drive decisions across teams, and own systems from design through production
Multi-tenant compute platforms, AI inference or training infrastructure, GPU workload scheduling
High-performance systems programming: async runtimes, zero-copy, memory-safe concurrency
Linux systems engineering and fleet automation: configuration management, kernel and driver debugging, safe upgrades, and automated recovery
Distributed filesystems like JuiceFS and Lustre
Experience with Nvidia/AMD GPU infrastructure; DCGM, NVLink, RDMA, InfiniBand/RoCEv2
Global networking and routing: BGP, ECMP, tunnels, eBPF, Geneve

Nice-to-haves

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airustpython

Platform: ashby
Effort: quick

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