Remote MLOps Engineer for Scalable ML Systems

mercor; United States; Teletrabalho; USD 96 000 - 152 000; Tempo parcial

Resumo da oferta

Mercor, headquartered in San Francisco, seeks an MLOps Engineer for a remote contract role. You will guide research and engineering teams on ML infrastructure, training pipelines, and framework-level optimization to push model performance.

The candidate should have 2+ years in ML infrastructure or MLOps at top-tier organizations, hands-on experience with JAX and/or PyTorch at scale, and kernel experience with Pallas (JAX) or Triton.

Qualificações

• 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.

Responsabilidades

• 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.

Conhecimentos

MLOps

JAX

PyTorch

Kernel optimization

Strong communication

Ferramentas

Pallas

Triton

Descrição da oferta de emprego

Mercor, headquartered in San Francisco, seeks an MLOps Engineer for a remote contract role. You will guide research and engineering teams on ML infrastructure, training pipelines, and framework-level optimization to push model performance.

The candidate should have 2+ years in ML infrastructure or MLOps at top-tier organizations, hands-on experience with JAX and/or PyTorch at scale, and kernel experience with Pallas (JAX) or Triton.