xAI

ML Infrastructure Engineer

Reposted 2 Months Ago

Palo Alto, CA, USA

In-Office

180K-440K Annually

Junior

Information Technology

The Role

Build and scale GPU compute infrastructure, training frameworks, and data pipelines. Integrate large-scale training and inference systems, productionize models with ML teams, ensure scalability and reliability, troubleshoot full-stack ML systems, and mentor junior engineers.

Summary Generated by Built In

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.

ML INFRASTRUCTURE ENGINEER

As an ML Infrastructure Engineer, you will play a pivotal role in building and optimizing the reliable, high-performance ML platform that powers recommendations on X. We're looking for exceptional engineers who are passionate about our mission and have a strong desire to make a meaningful impact.

RESPONSIBILITIES:

Designing, building, and scaling GPU compute infrastructure, training frameworks, and experimentation tools to enable rapid iteration on ML hypotheses
Developing data pipelines and integrating large-scale data, training, and inference systems
Collaborating with ML teams to productionize models and ensure seamless integration across the stack
Ensuring scalability, reliability, and efficiency of large-scale machine learning systems
Working across the full stack to solve complex problems independently
Mentoring junior engineers and contributing to the growth of the team

BASIC QUALIFICATIONS:

Bachelor's, master's, post-graduate, or Ph.D. degree in computer science, machine learning, or other quantitative discipline
2+ years of industry experience working with high traffic or large-scale production environments, distributed systems, GPU infrastructure, and/or deep learning applications
2+ years experience with ML platforms, training infrastructure, or close collaboration with modeling engineers and data scientists
Experience with Python and compiled languages such as C++ or Rust

PREFERRED SKILLS AND EXPERIENCE:

Deep familiarity with modern ML frameworks such as JAX or PyTorch
Low-level understanding of compute systems, including distributed storage, NVIDIA drivers, CUDA toolkits, and networking
Comfortable with Linux systems and orchestration tools
Experience with job schedulers (e.g., Slurm), configuration management (Puppet/Ansible), or related infrastructure tooling

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.

Skills Required

Bachelor, Master, Post-graduate or PhD in computer science, machine learning, or other quantitative discipline; or equivalent work experience
2+ years industry experience with high traffic or large-scale production environments, distributed systems, GPU infrastructure, and/or deep learning applications
2+ years experience with ML platforms, training infrastructure, or close collaboration with modeling engineers and data scientists
Strong proficiency with Python
Experience with compiled languages such as C++ or Rust
Deep familiarity with modern ML frameworks such as JAX or PyTorch
Low-level understanding of compute systems, including distributed storage, NVIDIA drivers, CUDA toolkits, and networking
Comfortable with Linux systems and orchestration tools
Experience with job schedulers (e.g., Slurm), configuration management (Puppet/Ansible), or related infrastructure tooling

xAI Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about xAI and has not been reviewed or approved by xAI.

Healthcare Strength — Healthcare is described as comprehensive, including medical, dental, vision, mental health support, and fertility benefits. Additional protections such as disability and life insurance, plus HSA/FSA options, are also outlined.
Equity Value & Accessibility — Equity is positioned as a core component of total rewards alongside high base salaries for technical roles. Pre‑IPO stock grants are highlighted as enabling participation in potential company growth.
Leave & Time Off Breadth — The package includes flexible or generous PTO, paid holidays, and paid sick days. Company materials also describe flexible vacation policies intended to help manage fast‑paced workloads.