Develop highly reliable, high-throughput inference systems that serve the best AI models internally across SpaceX

Architect and implement scalable distributed infrastructure for model serving, including load balancing, auto-scaling, batch scheduling, global KV cache, and continuous batching

Optimize latency and throughput of model inference under real production workloads, including low-level GPU kernel work, quantization, speculative decoding, and other acceleration techniques

Build reliable, high-concurrency serving systems with 100% uptime, low tail latency, and excellent observability

Own end-to-end components such as request routing, SDK development, rate limiting, and efficient scaling for internal SpaceX AI inference platforms

Benchmark, fine-tune, and accelerate inference engines (e.g., SGLang, vLLM, TensorRT-LLM)

Develop custom tools for tracing, replaying, and resolving issues across the full stack — from orchestration down to GPU kernels

Create robust CI/CD infrastructure for seamless endpoint deployment, image publishing, and inference engine updates

Collaborate across SpaceXAI teams to integrate inference capabilities into broader systems and workflows

Application Software Engineer

Responsibilities

Qualification

Required

Preferred

Benefits

Company

SpaceX

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