Role at a glance
- Salary
- Not Disclosed
- Location
- Palo Alto, California, United States
- Work arrangement
- On-site
- Employment
- Full-time
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Role Summary
The Member of Technical Staff - Inference designs and optimizes large-scale model-serving systems for Grok. The role spans distributed serving infrastructure, inference performance and reliability, deployment systems, and research on next-generation inference systems.
What You'll Do
- Architect and implement distributed model-serving infrastructure, including load balancing, auto-scaling, batch scheduling, and global...
- Optimize inference latency and throughput under production workloads, and build high-concurrency serving systems with attention to...
- Benchmark, fine-tune, and accelerate inference engines, including GPU kernel work and code generation.
- Develop tools to trace, replay, and fix issues across the serving stack, from orchestration to GPU kernels.
- Create CI/CD infrastructure for endpoint deployment, image publishing, and inference engine updates.
- Accelerate research on scaling test-time compute, RL rollout, and model-hardware co-design.
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View full postingQualifications
Basic qualifications include low-level systems programming in C/C++ or Rust; experience with large-scale, high-concurrency production serving, GPU inference engines, testing and benchmarking inference services, and designing and implementing CI/CD infrastructure for inference; and backgrounds in system, low-level inference, and algorithmic inference optimizations.
Required
- Low-level systems programming in C/C++ or Rust
- Experience with large-scale, high-concurrency production serving
- Experience with GPU inference engines such as vLLM, SGLang, Triton, or TensorRT-LLM
- Strong background in system optimizations including batching, caching, load balancing, and parallelism
- Low-level inference optimization skills including GPU kernels and code generation
- Algorithmic inference optimization skills including quantization, speculative decoding, distillation, and low-precision numerics
- Experience with testing, benchmarking, and reliability of inference services
- Experience designing and implementing CI/CD infrastructure for inference
Original job description
Content provided by the employer
Original job description
Content provided by the employer
SpaceXAI’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.
About the Role:
- 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
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.
BASIC QUALIFICATIONS:
- 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.
COMPENSATION AND BENEFITS:
$180,000 - $440,000 USD
Base salary is just one part of our total rewards package at SpaceXAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.
SpaceXAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.
About the company
xAI
Large Enterprise
xAI is a cutting-edge technology company focused on developing advanced artificial intelligence solutions to enhance human capabilities and optimize decision-making processes. Founded by a team of leading experts in AI and machine learning, xAI aims to address complex challenges across various industries, including healthcare, finance, and transportation. By prioritizing ethical AI development, the company is committed to creating innovative tools that empower organizations to harness the full potential of artificial intelligence while ensuring transparency and accountability.