Software Engineer - Training/Inference (C++)

xAI•Palo Alto, CA

1d•$180,000 - $440,000

About The Position

xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. 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

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.

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.