ML Systems Engineer

The ML Systems Engineer will guide research and engineering teams to close knowledge gaps and improve AI model performance in MLOps, training infrastructure, and ML framework-level topics. The role involves designing challenging tasks, writing solutions, evaluating tasks, and developing guidelines.

What You Will Do

The main day-to-day responsibilities include guiding research and engineering teams, designing tasks, writing solutions, evaluating tasks, and developing guidelines.

Why It Might Be a Fit

The ideal candidate will have 2+ years of experience in ML infrastructure, MLOps, or ML systems engineering, hands-on experience with JAX and/or PyTorch, and experience writing or optimizing custom GPU kernels using Pallas or Triton.

Requirements

• 2+ years of dedicated professional experience in ML infrastructure, MLOps, or ML systems engineering at a recognized, 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 the ability to explain complex technical decisions clearly.