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# Software Engineer - Training/Inference (C++)

- **Company:** Space X
- **Location:** Palo Alto, CA, United States
- **Salary:** $180,000.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Code Generation, Continuous Delivery, Continuous Integration, System Programming, Load Balancing, Autoscaling, Large Language Models, Caching, Parallel Computation, Discretization, Low Latency, SGLang, TensorRT
- **Published:** October 13, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3442649818&tx=JT10395UTD&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Deep low-level systems programming (C/C++ or Rust)
 * Experience with large-scale, high-concurrent production serving.
 * Experience with GPU inference engines (vLLM, SGLang, Triton, TensorRT-LLM, etc.).
 * Strong background in system optimizations: batching, caching, load balancing, parallelism.
 * Low-level inference optimizations: GPU kernels, code generation.
 * Algorithmic inference optimizations: quantization, speculative decoding, distillation, low-precision numerics.
 * Experience with testing, benchmarking, and reliability of inference services.
 * Experience designing and implementing CI/CD infrastructure for inference.

## Description

* We are building the high-performance inference platform that serves Grok to millions of users every day with lightning speed and perfect reliability.
 * As a Member of Technical Staff - Inference, you will design and optimize large-scale model serving systems end-to-end. You will own everything from distributed infrastructure (global KV cache, continuous batching, load balancing, auto-scaling) to deep low-level optimizations (GPU kernels, quantization, speculative decoding, tail latency).
 * This is a high-impact role where your work directly determines how fast and reliably users interact with Grok at massive scale, * Architect and implement scalable distributed infrastructure for model serving (load balancing, auto-scaling, batch scheduling, global KV cache).
 * Optimize latency and throughput of model inference under real production workloads.
 * Build reliable, high-concurrency serving systems that serve billions of users with 100% uptime, 0% error rate, and excellent tail latency.
 * Benchmark, fine-tune, and accelerate inference engines (including low-level GPU kernel work and code generation).
 * Develop custom tools to trace, replay, and fix 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.
 * Accelerate research on scaling test-time compute, RL rollout, and model-hardware co-design for next-generation systems.