Build and optimize the high-performance inference platform serving Grok at massive scale. Design distributed serving infrastructure, low-level GPU optimizations, quantization, speculative decoding, and CI/CD for production reliability and low latency.
180k – 440k
On-site5+ YOEML Engineering
About the role
Responsibilities
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.
Requirements
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.
Build and optimize the RL training framework and infrastructure for large-scale workloads at SpaceXAI, from ablations to production runs. Requires experience with distributed systems and proficiency in Python, JAX, Rust, or C++.
180k – 440k
On-site5+ YOEML Engineering
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