ML Infrastructure Engineer

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, 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

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 total compensation (base salary is just one part; package also includes equity, comprehensive medical, vision, and dental coverage, 401(k), disability insurance, life insurance, and perks)