# Software Engineer, Distributed Systems

**Company:** [Fal](https://hotfix.jobs/companies/fal)
**Location:** Remote
**Role:** Backend Engineering
**Experience:** 5+ years
**Skills:** Python, Rust, Distributed Systems, Orchestration, Scheduling, Gpu Autoscaling, Observability, Consensus Algorithms, Fault Tolerance, Capacity Planning, Ai/Ml Infrastructure, High-Performance Systems Programming, Networking, Gpu Workloads
**Posted:** 2026-03-13

> Build and scale fal's core Python/Rust distributed platform for AI inference, orchestration, GPU autoscaling, and low-latency global workloads. Requires 5+ years building large-scale distributed compute systems with strong fundamentals in consensus, scheduling, fault tolerance, and observability.

## Job Description

## Key Responsibilities
- Build our core Python/Rust platform: request routing, AI workload orchestration, scheduling, GPU autoscaling, large scale file storage, queueing, etc.
- Produce forward designs for platform evolution as we scale to 100x current traffic and need to provide low latency across the world.
- Leverage AI to an extreme level to automate the mundane parts of building complex but reliable systems.
- Profile and tune low level CPU and memory performance.

## Requirements
- 5+ years experience building distributed compute and orchestration platforms in Python or Rust.
- Strong understanding of distributed systems fundamentals: consensus, scheduling, fault tolerance, capacity planning.
- Deep understanding of computational complexity and memory allocation.
- Track record of designing systems that scale under real production load.
- Experience building and using observability to drive performance and reliability decisions.
- Excellent communication and ability to drive technical decisions across teams.
- Self-starter who executes quickly, takes ownership, and constantly seeks improvement.

## Nice-to-Haves
- Experience with AI/ML inference or training infrastructure.
- Experience with high-performance systems programming (async runtimes, zero-copy, memory-safe concurrency).
- Background in building multi-tenant compute platforms.
- Understanding of networking fundamentals and performance characteristics.
- Familiarity with GPU workload characteristics and scheduling constraints.

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