Back to jobs

Software Engineer - Training/Inference (C++)

Palo Alto, CA; Seattle, WA

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.

SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.

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 SpaceXSI, 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.

SpaceXSI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.

Create a Job Alert

Interested in building your career at SpaceXSI? Get future opportunities sent straight to your email.

Apply for this job

*

indicates a required field

Phone
Resume/CV*

Accepted file types: pdf, doc, docx, txt, rtf


If you are currently employed in the field, please tell us the name of your employer.

If you are currently employed in the field, please tell us your role including your seniority level (e.g. Software Engineer II).

If you have a public LinkedIn profile, please provide its URL.

If you have a Google Scholar page, please provide its URL.

Select...

In 100 words or less, tell us about a piece of work you are most proud of.

Select...