Applied AI Operations Engineer (LLM Apps)

SPACE EXPLORATION TECHNOLOGIES CORP; United States; On-site; USD 100,000 - 160,000; Full time

Benefits offered by this job

Stock options

Employee Stock Purchase Plan

Long-term incentives

Discretionary bonuses

Medical, vision, and dental coverage

401(k) retirement plan

Disability insurance

Life insurance

Paid parental leave

Paid vacation

Paid holidays

Sick leave

Job summary

SpaceX in the United States is seeking an Operations Engineer, Applied AI (Application Software) to partner with Sales, Product, and Engineering teams. You will design strategies for integrating large language models into products and infrastructure and build practical tests.

This role blends technical depth with collaboration, focusing on safe, responsible deployment and the delivery of AI-powered solutions across programs.

Qualifications

• Bachelor’s degree in a STEM or engineering discipline is required.
• 1+ years of engineering or operations/process development experience.

Responsibilities

• Work alongside internal teams to uncover needs and design technical approaches that align with goals.
• Serve as the main technical point of contact, supporting end-to-end while coordinating with internal teams to ensure strong outcomes.
• Build prototypes and proof-of-concept applications that illustrate effective ways to integrate with Spok/Grok.
• Advise on system-on-system architecture and support teams in developing AI-powered applications.
• Keep current evolving LLM technologies, best practices, and tools used in fast-moving environments.

Skills

Cross-functional teamwork

Production-level applications

LLM integration

Prompt design

Agent systems

Retrieval architecture

AI product design

Programming

Education

Bachelor’s degree in STEM or engineering

Tools

Spok/Grok

Python

Job description

SpaceX in the United States is seeking an Operations Engineer, Applied AI (Application Software) to partner with Sales, Product, and Engineering teams. You will design strategies for integrating large language models into products and infrastructure and build practical tests.

This role blends technical depth with collaboration, focusing on safe, responsible deployment and the delivery of AI-powered solutions across programs.