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
• 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

Benefits

• Health and wellness: Comprehensive health insurance including medical, dental, vision, and disability coverage
• Life and family: Life and AD&D insurance and fertility benefits to ensure our team’s well-being and peace of mind
• Flexible vacation: We work hard but avoid burn out. Take time off when you need it
• Visa sponsorship: We support international talent with visa sponsorship to join our team
• 401(k) plan: Retirement savings plan to secure your financial future

• We’re looking for exceptional engineers who are passionate about our mission and have a strong desire to make a meaningful impact
• 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
• Strong proficiency with Python and experience with compiled languages such as C++ or Rust
• 2+ years experience with ML platforms, training infrastructure, or close collaboration with modeling engineers and data scientists
• Low-level understanding of compute systems, including distributed storage, NVIDIA drivers, CUDA toolkits, and networking
• Comfortable with Linux systems and orchestration tools
• Deep familiarity with modern ML frameworks such as JAX or PyTorch
• Experience with job schedulers (e.g., Slurm), configuration management (Puppet/Ansible), or related infrastructure tooling