Member of Technical Staff, Inference
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.
About the job
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.
- 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.
Skills
C++, Rust, Gpu Kernels, vLLM, Sglang, Triton, Tensorrt-Llm, Quantization, Speculative Decoding, CI/CD, Load Balancing, Distributed Systems
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