GenAI MLOps Engineer - JAX/PyTorch & GPU Kernels

Dorado; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

Cincinnatus LLC is recruiting a talented MLOps Engineer to join a GenAI-focused team building foundational AI models. The role emphasizes training infrastructure, MLOps tasks, and kernel-level optimization, requiring hands-on work with JAX/PyTorch and GPU kernels.

The position is a 40-hour-per-week W-2 engagement, with no conflicts and potential placement at a leading AI Lab. Ideal for engineers who communicate clearly and sustain long-term commitments.

Qualifications

• 2+ years of dedicated professional experience in ML infrastructure, MLOps, or ML systems engineering.
• 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.

Responsibilities

• Guide research and engineering teams to close knowledge gaps and improve AI model performance in MLOps, training infrastructure, and ML framework-level topics.
• Design challenging, domain-relevant tasks, and write accurate and 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/evaluation frameworks to assess training pipeline design, distributed systems reasoning, and kernel-level optimization across tasks.
• Collaborate with other subject matter experts to ensure consistency and accuracy in training data.

Skills

MLOps

JAX/PyTorch

GPU Kernels

ML Infrastructure

Job description

Cincinnatus LLC is recruiting a talented MLOps Engineer to join a GenAI-focused team building foundational AI models. The role emphasizes training infrastructure, MLOps tasks, and kernel-level optimization, requiring hands-on work with JAX/PyTorch and GPU kernels.

The position is a 40-hour-per-week W-2 engagement, with no conflicts and potential placement at a leading AI Lab. Ideal for engineers who communicate clearly and sustain long-term commitments.