ML Infrastructure Engineer

USD 180,000-440,000 per year

MIDDLE SENIOR

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Ansible @ 3 CUDA @ 3 Communication @ 6 Data Pipelines Deep Learning @ 3 Distributed Systems @ 3 Experimentation GPU @ 3 JAX @ 2 Linux @ 3 Machine Learning @ 3 Networking @ 3 Prioritization @ 6 Puppet @ 3 PyTorch @ 2 Python @ 6 Rust @ 6 Slurm @ 3

Details

SpaceXAI is seeking an ML Infrastructure Engineer to build and optimize the reliable, high-performance machine learning platform that powers recommendations on X. The role involves working across the stack in a highly collaborative, hands-on engineering environment.

Responsibilities

Design, build, and scale GPU compute infrastructure, training frameworks, and experimentation tools to enable rapid iteration on machine learning hypotheses.
Develop data pipelines and integrate large-scale data, training, and inference systems.
Collaborate with machine learning teams to productionize models and ensure seamless integration across the stack.
Ensure the scalability, reliability, and efficiency of large-scale machine learning systems.
Work across the full stack to solve complex problems independently.
Mentor junior engineers and contribute to the growth of the team.

Requirements

Bachelor's, Master's, postgraduate, or PhD degree in computer science, machine learning, or another quantitative discipline, or equivalent work experience.
At least 2 years of industry experience working with high-traffic or large-scale production environments, distributed systems, GPU infrastructure, and/or deep learning applications.
At least 2 years of 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.
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.
Comfort working with Linux systems and orchestration tools.
Experience with job schedulers such as Slurm, configuration management tools such as Puppet or Ansible, or related infrastructure tooling.
Strong communication skills, work ethic, prioritization skills, curiosity, and a willingness to contribute directly to the company's mission.