Role OverviewGuide research and engineering teams to improve AI model performance in MLOps, training infrastructure, and ML framework-level topics. Design challenging tasks and write accurate solutions to MLOps and ML systems problems.
What You Will Do
Evaluate MLOps tasks and solutions, provide technical feedback, and develop guidelines and rubrics to assess training pipeline design and kernel-level optimization.
Why It Might Be a Fit
Must have 2+ years of experience in ML infrastructure, MLOps, or ML systems engineering, hands-on experience with JAX, and strong written communication skills.
Requirements
- 2+ years of dedicated professional experience in ML infrastructure, MLOps, or ML systems engineering
- Hands-on production experience with JAX at scale
- Experience writing or optimizing custom GPU kernels using Pallas or Triton
- Demonstrable career progression
- Ability to engage reliably for at least 40 hours/week during weekdays
- Strong written communication skills
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