Evaluation Infrastructure

xAI is hiring an Evaluation Infrastructure 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

Level rates this role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include GPU, distributed systems and llm inference.
Level classifies it as a senior role.
Level lists 296 other open roles at xAI.

AI in this role

Build and scale reliable evaluation infrastructure for cutting-edge AI language models at SpaceXSI.

gpu

distributed-systemsllm-inferenceworkload-schedulinginfrastructurepython

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 develop our evaluation infrastructure.

RESPONSIBILITIES:

Build highly reliable, efficient, and easy to use infrastructure that runs all our evaluations at scale
Collaborate closely with modeling & evaluation teams on developing new evals, maintaining evaluation signal quality, and building internal tooling to support training our next-generation of models
Identify and resolve performance bottlenecks in all layers of the eval infrastructure stack, including inference, capacity fleet management, asynchronous eval orchestration, and more

BASIC QUALIFICATIONS:

Experience in building, debugging, and optimizing efficiency of large-scale distributed systems
Willingness to dive deep and solve hard problems at all levels of the stack

PREFERRED SKILLS AND EXPERIENCE:

Experience in LLM inference
Experience in GPU compute management, workload scheduling, and dynamic resource optimization
Experience in developing interfaces for comparing models and evals

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

Evaluation Infrastructure at xAI rates 90 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

Evaluation Infrastructure 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 September 30, 2026.
Seniority: senior.
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

Distributed SystemsLLM InferenceWorkload SchedulingInfrastructurePythonGPU

Questions you could be asked

Tell me about a project where distributed systems was part of your work. What did you do?
Tell me about a project where llm inference was part of your work. What did you do?
Tell me about a project where workload scheduling was part of your work. What did you do?
Tell me about a project where infrastructure was part of your work. What did you do?
Tell me about a project where python was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Distributed Systems, LLM Inference, Workload Scheduling, Infrastructure, and Python. 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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