Remote MLOps Engineer—JAX/PyTorch, Triton Kernels

Weekday AI; United States; Remote; USD 96,000 - 152,000; Full time

Job summary

Weekday AI in the United States seeks an experienced MLOps Engineer to advance frontier AI systems. You will design and evaluate large-scale ML infrastructure, collaborate with researchers, and help build robust training pipelines using JAX, PyTorch, and custom GPU kernels.

This is a fully remote, 40-hour-per-week engagement. You will work with cross‑functional teams, review tasks, and contribute to performance optimization and scalable ML systems across the development lifecycle.

Qualifications

• Minimum 2 years of professional experience in MLOps, ML infrastructure, or ML systems engineering.
• Hands-on production experience with JAX and/or PyTorch in large-scale ML environments.
• Practical experience developing or optimizing custom GPU kernels using Pallas (JAX) or Triton.
• Strong understanding of distributed training systems, model optimization, and scalable ML infrastructure.
• Availability to work 40 hours per week during standard weekday business hours.

Responsibilities

• Partner with research and engineering teams to strengthen AI model capabilities in MLOps, ML infrastructure, and large-scale training systems.
• Design challenging, real-world MLOps and machine learning systems tasks that reflect production engineering scenarios.
• Develop accurate, well-documented solutions to complex ML infrastructure and training pipeline problems.
• Review and evaluate technical tasks and AI-generated solutions, providing clear and actionable written feedback.
• Create detailed evaluation rubrics and scoring frameworks for topics including: distributed training architectures, ML pipeline design, infrastructure optimization, kernel-level programming, and performance tuning.

Skills

MLOps

JAX

PyTorch

GPU kernels

Pallas

Triton

Distributed training

ML infrastructure

Tools

Pallas

Triton

Job description

Weekday AI in the United States seeks an experienced MLOps Engineer to advance frontier AI systems. You will design and evaluate large-scale ML infrastructure, collaborate with researchers, and help build robust training pipelines using JAX, PyTorch, and custom GPU kernels.

This is a fully remote, 40-hour-per-week engagement. You will work with cross‑functional teams, review tasks, and contribute to performance optimization and scalable ML systems across the development lifecycle.