The opportunity
SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.
What you'll do
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)
What they're looking for
- 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
- Bachelor's degree in computer science, engineering, math, or scientific: discipline; OR 2+ years of professional experience building software in lieu of a degree