Responsibilities
• Guide research and engineering teams to close knowledge gaps in MLOps, training infrastructure, and ML framework-level topics
• Design challenging, domain-relevant tasks and write accurate, well-structured solutions to MLOps and ML systems problems
• Evaluate MLOps tasks and solutions and provide clear, written technical feedback
• Develop guidelines and detailed rubrics to assess training pipeline design, distributed systems reasoning, and kernel-level optimization
• Collaborate with other subject matter experts to ensure consistency and accuracy in training data

Requirements
• 2+ years of dedicated professional experience in ML infrastructure, MLOps, or ML systems engineering at a top-tier organization
• Hands-on production experience with JAX and/or PyTorch at scale
• Experience writing or optimizing custom GPU kernels using Pallas (JAX) or Triton
• Demonstrable career progression
• Ability to engage reliably for at least 40 hours/week during weekdays
• Strong written communication skills and ability to explain complex technical decisions clearly

How to Apply
Click "Apply" to be taken to the Cincinnatus LLC application. This is a full-time W-2 contingent role.