ML Infrastructure Engineer

Highlights

• Build the high-performance ML platform powering recommendations on X.
• Scale reliable systems enabling rapid iteration on ML hypotheses.
• Solve complex problems independently with end-to-end ownership.

What they offer

• Equity
• Comprehensive medical, vision, and dental coverage
• 401(k) retirement plan
• Short and long-term disability insurance
• Life insurance
• Discounts and perks

Ideal candidate

Hands-on, curious engineer who thrives in flat, high-ownership teams and cares about understanding the universe through AI. Motivated by engineering excellence, strong prioritization, and clear communication to deliver 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 with high-traffic or large-scale production environments, distributed systems, GPU infrastructure, and/or deep learning
• 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
• 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 such as Slurm, configuration management like Puppet/Ansible, or related infrastructure tooling