RL Inference

xAI is hiring an RL Inference in Palo Alto, United States. It pays $180k-$440k a year and Level rates it ; you can apply on xAI.

Level's read on this job

On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
The posting mentions vLLM, PyTorch and JAX among its tools and skills.
xAI has 296 other open roles on Level.

AI in this role

vllmpytorchjax

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:

The RL infrastructure team is looking for an engineer to help with low precision RL training and inference.

RESPONSIBILITIES:

Design and optimize our inference stack for all shapes of RL workloads at SpaceXSI, from small scale ablations to production training runs.
Analyze, profile and address performance bottlenecks in large scale RL systems
Work closely with the modelling team to efficiently implement novel RL techniques and algorithms

BASIC QUALIFICATIONS:

Experience in building, debugging, and optimizing efficiency of large-scale distributed systems
Experience in LLM inference
Proficiency in programming languages such as Python, C++ and/or Rust; frameworks such as PyTorch, Jax, CUDA
Willingness to dive deep and solve hardcore problems at all levels of the stack

PREFERRED SKILLS AND EXPERIENCE:

Strong knowledge in quantization and numerics in LLM inference and training
Experience in developing inference engines, e.g. SGLang, vLLM

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.

How we rate this

RL Inference at xAI rates 94 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

RL Inference is an open job at xAI.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $180k-$440k a year.
The role is based in Palo Alto, CA.
The job was posted on July 6, 2026.
Applications are handled by xAI. Apply on xAI

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

vLLMPyTorchJAX

Questions you could be asked

What's a project where you used vLLM hands-on?
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of JAX that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: vLLM, PyTorch, and JAX. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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