ML Infrastructure Engineer

xAI•Palo Alto, CA

21h

About The Position

SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. ABOUT THE ROLE: 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.

Requirements

Bachelor, Master, Post-graduate or PhD in computer science, machine learning, or other quantitative discipline; or equivalent work experience
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
Strong proficiency with Python and experience with compiled languages such as C++ or Rust

Nice To Haves

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

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