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Software Engineer, Distributed Systems

fal.ai· Engineering
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San Francisco$180k - $250kFull-timePosted 3mo ago

About the role

Experienced software engineer focused on building large-scale computing platforms with reliability, performance, and scalable operations for AI inference and GPU computing.

Responsibilities

  • 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
  • Specialize in one or more of: 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

  • 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

Skills

Required:RustPythonDistributed systemsObservability

Benefits

  • Interesting and challenging work
  • A lot of learning and growth opportunities
  • Regular team events and offsites
Last updated Oct 17, 2026. Interested? Apply on the company site.

About fal.ai

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