Joblaze summary

In this role, the software engineer focuses on developing and enhancing a robust distributed systems platform that supports high-performance AI workloads. Proficiency in Python or Rust is essential, along with a solid grasp of distributed systems principles such as fault tolerance and scheduling. This position is ideal for experienced engineers who have a proven track record in building scalable systems under production loads and are eager to drive technical decisions. The team at fal is dedicated to pushing the boundaries of generative media, making this an exciting opportunity in a rapidly evolving field.

Joblaze insights

Listed about a month ago — first seen on Joblaze September 3, 2026. Last confirmed on Fal's careers page October 29, 2026.
This exact title is also open at 1 other location at Fal: Remote - Global.
Salary band is in line with the typical range for Backend roles (median ~$180,000).
Starts above 89% of 56 comparable mid backend roles in United States that list Python we track (median $146,000 across 27 companies). See Python salary trends
Python appears in 43% of 256 comparable mid backend roles in United States; Rust appears in 14.5% of 256 comparable mid backend roles in United States.

Quick facts

Is the Software Engineer, Distributed Systems role remote?: No — this is an on-site role in San Francisco.
What's the salary range?: Fal lists $180,000–$250,000 for this role.
How much experience is required?: At least 3 years of relevant experience for this Software Engineer, Distributed Systems role.
Where is the role based?: Fal is hiring for this position in San Francisco.
What's the tech stack?: Joblaze extracted these technologies from the posting: Python, Rust.
What seniority level is this role?: Fal targets mid-level candidates for this position.
Is this full-time or contract?: Full-time for this Software Engineer, Distributed Systems role at Fal.

From the original posting

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

Standard company text repeated across Fal's postings is omitted here.

Software Engineer, Distributed Systems

About this role:

What you'll do:

Qualifications/Nice to have:

What we offer at fal:

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